Republic of Iraq

Ministry of Higher Education & Scientific Research

Al-Nahrain University

College of Science

Effect of Band Ratios and Indices on Classification Accuracy of Multispectral Satellite

Images

A Thesis

Submitted to the College of Science of Al-Nahrain University as a Partial Fulfillment of the Requirements for the Degree of Master of

Science in Physics

By

Saif Kamil Shnain

(B.Sc. 2005)

Supervised by

Professor Dr. Ayad A. Al-Ani Assistant Professor Dr. Salah A. Saleh

March 2008 Safar 1429

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Acknowledgments

Praise to our almighty GOD to the most merciful and gracious

for enabling me to complete this thesis.

I would like to express my sincere thanks and deep gratitude

to my supervisors Professor Dr. Ayad A. Al-Ani and Assistant

Professor Dr. Salah A. Saleh for supervising the present work and

for their support, encouragement and suggestions throughout the

work.

Special thanks and deep affection to my family especially my

parents for their love, help, and supports through the years.

Finally, I would like to extend my thanks and gratitude to all

who had ever assisted me during the period of this research.

ft|y ^tÅ|Ä

 

Abstract Multispectral satellite images are holding whole information about the area

under investigation. Many of digital processing techniques are used to extract

most of the possible information from these images, such these techniques are

spectral band ratios, Tasseled Cap transformation and Principle Component

Analysis (PCA). These techniques are play good part for features extraction and

reducing the number of spectral bands with no loses in information.

The research aims to apply classifications on band ratios and image indices

which include PCA and Tasseled Cap transformations, of Enhanced Thematic

Mapper Plus (ETM+) satellite images, and to show how the accuracies of the

classification are differ from the accuracy of classification of the raw ETM+ bands

images.

The results show that the best bands ratios that adopted to represent five

selected land features are 5/7, 4/5, 2/5, 1/4, and 2/4. Optimum Index Factor

method (OIF) has been adopted on raw bands combinations, and on the bands

ratio combinations to select which band combination that contains much

information, where the results show that the best combination for raw bands is

3-4-7, and best combination for ratios band is 4/5-1/4-2/5.

Iterative Self Organizing Data Analysis (ISODATA) method has been

adopted as unsupervised classification, and Maximum Likelihood method as

supervised classification. The overall accuracies are 87.18%, 89.45%, 89.26%,

and 89.52% for raw bands, band ratios, Tasseled Cap, and PCA respectively,

which showed that transformation techniques give good spectral enhancements

and feature extraction which are undistinguishable in raw image, and represent

good tools to increase the accuracy of classification.

i

 

Contents

Title Page

Contents………….…………….…………….……………………….……..i

List of figures………………………………………………………………iv

List of tables…….….…………………….………………………………...vi 

Abbreviations…………………………………………………….....…….vii

Chapter One: Introduction

1.1 Introduction……………………………………………………….1

1.2 Previous Researches …………………………...…………………2

1.3 The Study Area…………………………………………………...4

1.4 Aim of the Thesis…………………………………………………6

1.5 Thesis Organization………………………………………………6

Chapter Two: Basic Principle of Remote Sensing

2.1 Introduction……………………………………………………….7

2.2 Electromagnetic Energy…………………………………………..7

2.3 Spectral Reflectance……………………………………………..10

2.4 Sensor Resolution……………………………………………….12

2.5 Remote Sensing Sensors………………………………………...14

2.6 Landsat Systems ………………………………………………...16

2.6.1 Landsat 1, 2, and 3……………………………………...17

2.6.2 Landsat 4 and 5…………………………………………17

2.6.3 Landsat 6………………………………………………..18

2.6.4 Landsat 7………………………………………………..18

ii

 

2.6.5 Characteristics of ETM+ Spectral Bands………………22

Chapter Three: Bands Transformation Techniques & Classification

3.1 Introduction……………………………………………………...24

3.2 Multispectral Remote Sensing Images………………………......24

3.3 Band Ratios……………………………………………………...25

3.4 Vegetation Indices……………………………………………….29

3.5 Spectral Indices............................................................................30

3.6 Separating Soil Reflectance from Vegetation Reflectance……...31

3.7 Tasseled Cap Transformation…………………………………...33

3.8 Principle Component Analysis (PCA)…………………………..36

3.8.1 Mathematical Representation…………………………..38

3.9 False Color Composite (FCC)…………………………………...40

3.9.1 Selecting the Best Color Composites…………………..40

3.10 Multispectral Classification……………………………………42

3.10.1 Unsupervised Classification…………………………..42

3.10.2 Supervised Classification…..………………………….44

Chapter Four: Results & Discussion

4.1 Introduction……………………………………………………...48

4.2 Band Ratios……………………………………………………...51

4.3 Tasseled Cap Transformation…………………………………...58

4.4 Vegetation indices……………………………………………….60

4.5 Principle Component Analysis…………………………………..63

4.6 False Color Composites (FCC)………………………………….68

4.7 Images Classification……………………………………………73

4.7.1 Unsupervised Classification……………………………73

iii

 

4.7.2 Supervised Classification……………………………….76

4.7.3 Classification Accuracy Assessment…………………...80

Chapter Five: Conclusions & Recommendations

5.1 Conclusions……………………………………………………...83

5.2 Recommendations……………………………………………….84

References…………………………………………………………………85

Appendix…………………………………………………………………..90

iv  

List of figures

Figure Title Page

(1-1) Location of study area 5

(2-1) Spectral reflectance curve 11

(2-2) Examples of remote sensing systems with spectral resolution 13

(3-1) Reduction of scene illumination effects through spectral ratioing 27

(3-2) Soil brightness line 32

(3-3) Seasonal variation of a field is defined by Greenness and 34

Brightness

(3-4) 2D scatter plot of two Tasseled Cap components 35

(3-5) PCA reduce the dimensionality 37

(4-1) 6 non thermal ETM+ band images with their histograms 48

(4-2) The scheme of the work 50

(4-3) 5/7 band ratio for water 53

(4-4) 4/5 band ratio for vegetations 54

(4-5) 2/5 band ratio for bare lands 55

(4-6) 1/4 band ratio for urban areas 56

(4-7) 2/4 band ratio for crop lands 56

(4-8) The first three Tasseled Cap transformation indices 59

(4-9) NDVI for the study area 60

(4-10) Spectral indices images 62

(4-11) PCA images with their histograms 65

(4-12) FCC images for different combinations 72

v  

(4-13) Unsupervised classification of different images 75

(4-14) Supervised classification 78

vi  

List of tables

Table Title Page

(2-1) Electromagnetic spectral regions 9

(2-2) Sensor systems 15

(2-3) Characteristics of Landsat 1 to 7 missions 20

(2-4) Selected current remote sensing systems and their major 21

characteristics

(4-1) The best band ratios for certain features 52

(4-2) Multivariate statistics of the band ratios 57

(4-3) Correlation between band ratios 57

(4-4) Multivariate statistics of the original bands 67

(4-5) Covariance-variance matrix 67

(4-6) Correlation between bands 68

(4-7) Ranked OIF values of raw band combinations 70

(4-8) Ranked OIF values of ratio bands combinations 71

(4-9) results of supervised classification of different band combinations 77

(4-10) Confusion Matrices 81

vii

 

Abbreviations

BSI: Bare Soil Index

DN: Digital Number

E: Electric field

EMR: Electromagnetic Radiation

ENVI: Environment for Visual Images

ERTS: Earth Resources Technology Satellite

ETM+: Enhanced Thematic Mapper plus

FCC: False Color Composite

GIS: Geographic Information System

H: Magnetic field

IR: Infra Red

ISODATA: Iterative Self Organizing Data Analysis

Landsat: Land Satellite

MIR: Mid Infra Red

MSS: Multispectral Scanner

NASA: National Aeronautics and Space Administration

NDVI: Normalized Difference Vegetation Index

NDWI: Normalized Difference Water Index

NIR: Near Infra Red

OIF: Optimum Index Factor

PCA: Principle Component Analysis

R: Red

viii

 

RBV: Return Beam Vidicon

RGB: Red-Green-Blue (color image)

SAVI: Soil Adjust Vegetation Index

SWIR: Short Wave Infra Red

TC: Tasseled Cap

TM: Thematic Mapper

UI: Urban Index

UV: Ultra Violet

CHAPTER ONE

INTRODUCTION

 

1

 

1.1 Introduction

Remote sensing is defined as the acquisition of information about an

object without being in physical contact with it. Information is acquired by

detecting and measuring changes that the object imposes on the surrounding

field [1]. The advantage of gathering data about objects remotely are that the

object is usually not disturbed objects in inaccessible areas can be examined,

and a large amount of information over any spatial area can be collected [2].

The detection and recording instruments for this special technology are

known collectively as remote sensors and include photographic cameras,

mechanical scanners, and radar systems. Remote sensors are typically carried

on aircraft and earth-orbiting spacecraft, which have led to the familiar phrase

“eye in the sky” [3], so that a remotely sensed image is a spatial representation

of surface of the earth.

The development of image sensor technology has made it possible to

capture image data in multi bands covering a broad spectrum of wavelength

range [4], i.e. the same scene was imaged simultaneously in several bands,

although satellite and airborne multispectral sensors provide data in the form of

several images of the same area of the earth’s surface, but taken through

different spectral windows or bands. The number of spectral bands varies, but

typically ranges from 4, as with Landsat 4 to 350 bands or more for many

satellite sensors [5].

The rich information available in multispectral imagery has posed

significant opportunities and challenges for feature extraction and classification.

So an alternative way is to use simple features that are physically meaningful,

one such feature that has received much attention in the remote sensing

community is the band ratios, the ratio of spectral values between two different

2

 

bands. Band ratios have been used for many years in the remote sensing

community to identify terrain cover types [4].

Many algorithms have been proposed for classification purpose, such as

Principle Component Analysis (PCA), which is not just a good tool for data

compression purposes but also as a good tool for feature extraction which, in

turn, play an important role in image classification and recognition techniques

[2]. The other algorithms are spectral indices (NDVI, BSI, NDWI, and UI) and

Tasseled Cap transformation. The Tasseled Cap transformation is a useful tool

for compressing spectral data into a few bands associated with physical scene

characteristics [6].

1.2 Previous Researches

Band ratios and indices are important data transformation techniques, used

in remote sensing work with multispectral data, which are represent the good

tool for classification, image enhancement and data extraction.

In 1989, Chaves [2] applied selective PCA for extracting spectral contrast

in Landsat TM image data. The result shows that selective PCA can be used to

enhance and map the spectral differences or contrast between different spectral

regions.

In 1996, Al-Ani [7] applied principle component analysis on six TM

satellite images for enhancement and color image composition. The researcher

applied maximum likelihood method to classify the TM satellite images. The

results show that the over all classification accuracy is 83%.

3

 

In 1999, Frankovich [8] used ISODATA unsupervised classification

method on PCA and Tasseled Cap transformation of the six TM satellite images

when he merged them layers into one image). The overall classification

accuracy is 88%.

In 2002, Al-Sepahe [2] used supervised and unsupervised classification to

classify selective original TM image bands and principle component images,

the researcher showed how the selective images investigate high classification

accuracy with minimum selected number of images, and suggested a new

method to select the type and the number of bands which verify high

classification accuracy based on maximum likelihood.

Abdeen et al [9] used ASTER band ratio images in geological mapping,

and they compared the results with established Landsat ETM+ band

combinations and ratio images. They used OIF method to select the best R-G-B

ratio combination which has the most spectral information.

In 2003, Inzana et al [10] used supervised classification of Landsat TM

band ratio images, and Landsat TM band ratio image with radar images. The

overall accuracy of classification of the radar images is 91.23% which is better

than the accuracies of the traditional TM and radar fused image products, which

their accuracies are 89.34% and 89.03% respectively.

In 2005, Feely et al [11] applied spectral indices (NDVI, IRI, and MIRI)

on Landsat ETM+ bands to examine the utility of these indices for measuring

various forest attributes in the semi-deciduous tropical dry forests on study area.

Fu et al [4] used band ratio on hyperspectral images, and they showed the

potential of spectral band ratio featured for the accurate pixel classification

when they adopted the boosting frame work for the selection of multiple ratio

features.

4

 

Thapa et al [12] used SAVI, BSI, NDWI, and UI indexes, were computed

in multi-temporal Landsat images; to analyze the environmental changes in

respect of vegetation, agriculture, water, and urban activities of Vietnam.

In 2006 Fadhil [13] applied NDVI, BSI, NDWI, and Tasseled Cap indices

on multi-temporal Landsat images; his study demonstrates the effectiveness of

the remote sensing and GIS technologies in detecting, assessing, mapping, and

monitoring the environmental changes.

In 2007, Buhe et al [14] indicated that R-G-B color overly using

atmospheric corrected ASTER original bands 2,3 and 6 which has the highest

OIF, but when they considered NDVI as one ASTER band, the highest OIF was

carrying out bands 3,4 and NDVI which was the best three-band combinations

for supervised and unsupervised classification.

1.3 The Study Area

The study area is Al-Kut city and its neighboring areas. The centre of

Al-Kut city is located at latitude 30º 30′ N and longitude 45 º 49′ E, it is the city

in the eastern Iraq , on Tigris river on the junction between the river and the

distribution canal Shatt Al-Gharraf, about 100 miles south east of Baghdad.

Close to the city lies the Kut Barrage, this distributes river water into irrigation

canals, figure (1-1).

The old town of Al-Kut is within a sharp “U” bend of the river, almost

making it an island but for a narrow connection to the shore. As of 2003 the

estimated population is about 400.000 people, the economy of Al-Kut is based

upon the rich agriculture of the region, of which Al-Kut is the main trade and

administrative centre [15].

5

 

 

The study was performed using Landsat ETM+ images (six no thermal

bands) of study area (path 167, row 38) acquired in 15th September 2002.

     

                   

 

 

       

 

 

                                                

Figure (1-1): Location of study area

6

 

1.4 Aim of the Thesis

The aims of this thesis are to apply classification on original bands, band

ratios, and image indices (PCA and Tasseled Cap transformation) of Enhanced

Thematic Mapper plus (ETM+) satellite images, and to show how the

accuracies of this classification are differ from the accuracy of the classification

of the original ETM+ bands. Supervised and unsupervised methods will adopt

for the classification purpose.

The problem has been come from selecting best band combination for

classification process, so that the Optimum Index Factor method (OIF) is used,

such that high OIF values indicate bands that contain much information.

Some spectral indices NDVI, BSI, NDWI, and UI will be used which will

derive from ETM+ bands for monitoring the land cover in the study area.

1.5 Thesis Organization

In addition to this chapter the thesis consists of other four chapters

outlined as follows:

Chapter Two describes the theoretical concepts and fundamentals of remote

sensing techniques.

Chapter Three present band transformation techniques and describes the

classification methods, supervised and unsupervised classification methods

have been adopted.

Chapter Four illustrates the results and their discussion.

Chapter Five includes the conclusions and recommendations for the future

studies.

CHAPTER TWO

BASIC PRINCIPLE OF REMOTE SENSING

 

7

 

2.1 Introduction

Remote sensing became possible with the invention of the camera in the

nineteenth century. Astronomy was one of the first fields of the science to

exploit this technique, and to this day much of astronomy is inextricably linked

with remote sensing [16].

The advent of the space age opened a whole new dimension in our ability

to observe, study, and monitor planetary (include Earth) surfaces and

atmospheres on a global and continuous scale. This led to major developments

in the field of remote sensing, both in its scientific and technical aspects [1].

2.2 Electromagnetic Energy

The term remote sensing is restricted to methods that employ

electromagnetic energy as the means of detecting and measuring target

characteristics. Electromagnetic energy includes light, heat, and radio waves

[17].

Energy recorded by remote sensing systems undergoes fundamental

interactions that should be understood to properly interpret the remotely sensed

data. For example, if the energy being remotely sensed comes from the sun, the

energy [18]:

• Is radiated by atomic particles at the source (the Sun),

• Propagates through the vacuum of space at the speed of light,

• Interacts with the Earth’s atmosphere,

• Interacts with the Earth’s surface,

8

 

• Interacts with the Earth’s atmosphere once again, and

• Finally reaches the remote sensor, where it interacts with various optical

systems, filters, film emulsions, or detectors.

Electromagnetic Radiation (EMR) consists of an electrical field (E) that

varies in magnitude in a direction perpendicular to the direction of propagation.

In addition, a magnetic field (H), oriented at right angles to the electrical field,

is propagated in phase with electrical field [19].

The electromagnetic spectrum is the continuum of energy the speed of

light. Several regions of the EM spectrum are of particular interest for remote

sensing visible, infrared, and microwave regions. Table (2-1) illustrates a

simple description to each part in electromagnetic spectrum [17].

9

 

Region Wavelength Remarks

Gamma ray < 0.03 nm Incoming radiation is completely absorbed by the upper atmosphere

and it’s not available for remote sensing.

X-ray (0.03-3.0) nm Completely absorbed by atmosphere. Not employed in remote sensing.

Ultraviolet (0.03-0.4) μm Transmitted through atmosphere. Detectable with film and

photodetectors, but atmospheric scattering is severe.

Photographic

UV band (0.3-0.4) μm

Transmitted through atmosphere. Detectable with film and

photodetectors, but atmospheric scattering is severe.

Visible (0.4-0.7) μm Imaged with film and photodetectors. Includes reflected energy peak of

earth at 0.5 μm.

Infrared (0.7-100) μm Interaction with matter varies with wavelength. Atmospheric

transmission windows are separated by absorption bands.

Reflected IR

band (0.7-3.0) μm

Reflected solar radiation that contains no information about thermal

properties of materials. The band from 0.7 to 0.9 μmis detectable with

film and is called the photographic IR band.

Thermal IR

band

(3-5) μm

(8-14) μm

Principle atmospheric windows in the thermal region. Images at these

wavelengths are acquired by optical-mechanical scanners and special

vidicon systems but not film.

Microwave (0.1-30) cm Longer wavelengths can penetrate clouds, fog and rain. Images may be

acquired in the active or passive mode.

Radar (0.1-30) cm Active of microwave remote sensing. Radar images are acquired at

various wavelength bands.

Radio >30 cm Longest wavelength portion of electromagnetic spectrum. Some

classified radars with very long wavelength operate in this region.

Table (2-1): Electromagnetic spectral regions [17]

10

 

2.3 Spectral Reflectance

The reflectance characteristics of earth surface features may be quantified

by measuring the portion of incident energy that is reflected. This is measured

as a function of wavelength and is called spectral reflectance ρ λ where is

expressed as percentage. It is mathematically defined as [20]:

                                                                 (2-1)

A graph of the spectral reflectance of an object as a function of

wavelength is termed a spectral reflectance curve. The configuration of spectral

reflectance curves gives us insight into the spectral characteristics of an object

and has a strong influence on the choice of wavelength region(s) in which

remote sensing data are acquired for a particular application [20]. In principle,

material can be identified from its spectral reflectance signature if the sensing

system has sufficient spectral resolution to distinguish its spectrum from those

of other materials. This premise provides the basis for multispectral remote

sensing [21]. Figure (2-1) shows typical spectral reflectance curves for different

characteristic of earth surface materials [20].

11

 

The reflectance of clear water is generally low. However, the reflectance is

maximum at the blue end of the spectrum and decreases as wavelength

increases. The reflectance of bare soil generally depends on its composition,

where increases monotonically with increasing wavelength. Whereas vegetation

has a unique spectral signature that enables it to be distinguish readily from

other types of land cover. The reflectance is low in both the blue and red

regions of the spectrum and high in the near infrared [16].

Figure (2-1): Spectral reflectance curve [20]

12

 

2.4 Sensor Resolution

Each remote sensing system has four major resolutions associated with it.

These resolutions should be understood by the scientist in order to extract

meaningful information from the remotely sensed imagery. Resolution (or

resolving power) is defined as a measure of the ability of an optical system to

distinguish between signals that are spatially near or spectrally similar. The

types of resolution are as follows [18]:

1. Spectral Resolution

This refers to the number and dimension of specific wavelength intervals

in the electromagnetic spectrum to which a remote sensing instrument is

sensitive [18]. Most remote sensing systems are multispectral and obtain data at

a number of wave bands.

Figure (2-2) shows examples of systems that obtain data in one, three, and

six bands. Each layer of data is referred to as a band, for examples Landsat

Multispectral Scanner (MSS) obtained data in four bands, and each pixel

consequently has four digital numbers associated with it, whereas the Landsat

Thematic Mapper (TM) system is a seven band system. Thus Landsat (TM) has

a better spectral resolution than Landsat (MSS) [16].

13

 

2. Spatial Resolution

There is a relationship between the size of a feature to be identified and

the spatial resolution of the remote sensing system. Spatial resolution is a

measure of the smallest linear separation between two objects that can be

resolved by the sensor [18].

3. Temporal Resolution

Temporal resolution of a remote sensing system refers to how often it

records imagery of a particular area [18]. It is generally surveys which tend to

take place at infrequent intervals, usually for specific projects [16].

Figure (2-2): Examples of remote sensing systems with spectral resolution [16]

14

 

4. Radiometric Resolution

Radiometric resolution of a remote sensing system is a measure of how

many grey levels are measured between pure black (which could represent no

reflectance from the surface) and pure white. The radiometric resolution is

measured in ‘bits’ [16].

2.5 Remote Sensing Sensors

In remote sensing, the sensor measured energy, whereby the

distinguishing between passive and active techniques must be explained.

Passive remote sensing techniques employ natural sources of energy, such as

the Sun. Passive sensor systems based on reflection of the Sun’s energy can

only work during daylight. Passive sensor systems that measure the longer

wavelengths related to the Earth’s temperature does not depend on the Sun as

the source of illumination and can be operated at any time. Active remote

sensing techniques, for example radar and laser, have their own source of

energy. Active sensors emit a controlled beam of energy to the surface and

measure the amount of energy reflected back to the sensor. The main advantage

of active sensor systems is that they can be operated day and night, have a

controlled illuminating signal, and are typically not affected by the atmosphere

[22]. Table (2-2) shows the classification of sensors.

15

 

      

 

 

 

 

 

 

PASIVE SENSORS

(Detect the reflected or emitted radiation from natural sources)

ACTIVE SENSORS

(Detect reflected responses from objects that are irradiated from

artificially generated energy sources such as radar)

Non-Scanning • Non-Imaging: (they are types of

profile recorder, examples: Microwave sensor, Gravimeter, Spectrometer)

• Imaging: (Examples of this are the cameras which can be monochromatic, Natural Color, Infrared etc.)

Scanning • Imaging: 1- Image plane scanning, example,

TV camera solid scanner 2- Object plate scanning, example,

and Optical Mechanical scanner.

Non-Scanning • Non-Imaging: (they are types of

profile recorder, examples: Microwave Radiometer, Microwave Altimeter, Laser Water Depth, Laser Distance Meter)

• Imaging: (it is Radar, examples, object plane scanning

1- Real Aperture Radar 2- Synthetic Aperture Radar

Scanning • Image plane scanning: 1-Passive Array Radar.

Table (2-2): Sensor systems [1]

16

 

2.6 Landsat Systems  

In 1967, the National Aeronautics and Space Administration (NASA),

encouraged by the U.S. Department of the Interior, initiated the Earth Resource

Technology Satellite (ERTS) program [18].

On 23 July 1972, the first civilian satellite dedicated to obtaining repetitive

remote sensing data of the Earth with a spatial resolution of less than 100m was

launched [16]. The first two satellites launched were not called Landsat. Their

original designations were: ERTS–A and ERTS–B (Earth Resources

Technology Satellite). Prior to the launch of ERTS–B the name Landsat was

adopted. ERTS–A was renamed as Landsat 1 and ERTS–B left the launch pad

as Landsat 2 [23].

Satellite systems have a number of advantages and disadvantages

compared with aerial remote sensing. A single Landsat scene covering an area

of 185×185 km2 is the equivalent of around 1000 photographs. To form all

these photographs which may have been taken under varying lighting

conditions into a mosaic is impracticable. However, satellite images allow large

areas that have been imaged in very short time period to be analysed

synoptically. Scale distortions are minimised because topographic variations are

very low compared with the latitude at which the satellite revolves, and only a

small rotation in the mirror angle is required to image a wide area [16].

17

 

2.6.1 Landsat 1, 2, and 3

The first Landsat scene consisted of three satellites that were launched

between 1972-1978 at a nominal altitude of 918 km, this orbit did not prevent

obtaining images for a 185 km wide swathand global coverage every 18 days.

The orbital plane for a Landsat satellite carried two types of imaging

systems, these are [16]:

• A Return Beam Vidicon (RBV): it obtained data in 3-bands with 80m

resolution; it has not been used in the second Landsat series.

• Multispectral Scanner (MSS): that obtained data in four wavebands

corresponding to the green (band 4), red (band5), infrared (band 6 and 7),

and thermal infrared (band 8).

2.6.2 Landsat 4 and 5

Landsat 4 and 5 are the second generation of Landsat series launched in

July 16, 1984 and March 1, 1984, respectively [17]. Landsat 4 and 5 carried a

replacement for MSS known as the Thematic Mapper (TM), which can be

considered as an upgraded MSS. In these satellites, both the TM and MSS were

carried on an improved platform that maintained a high degree of stability in

orientation as a means of improving geometric qualities of the imagery [19].

18

 

2.6.3 Landsat 6

The Enhanced Thematic Mapper (ETM), planned as a replacement for

Landsat 6, was designed to acquire imagery over a 185 km swath, with a 16 day

coverage cycle. It was designed to acquire seven channels of data in the visible,

near infrared, shortwave infrared and thermal regions. The thermal band would

have a resolution of 120 m, the others 30 m, Plus an additional panchromatic

band at 15 m. The ETM was lost with the failed launch of Landsat 6 in

September 1993 [19].

2.6.4 Landsat 7

The LANDSAT 7 satellite was successfully launched in April 15, 1999.

LANDSAT 7 is a 5000 pound-class satellite, designed with a 16-day repeat

cycle. The payload is a single nadir-pointing instrument, the Enhanced

Thematic Mapper Plus (ETM+) [24].

Landsat 7 has a unique and essential role in the realm of earth observing

satellites in orbit. No other systems match Landsat's high spatial resolution,

spectral range and radiometric calibration. In addition, the Landsat Program is

committed to provide Landsat digital data to the user community in greater

quantities, more quickly and at lower cost than at any previous time in the

history of the program.

The earth observing instrument on Landsat 7, the Enhanced Thematic

Mapper Plus (ETM+), replicates the capabilities of the highly successful

Thematic Mapper instruments on Landsats 4 and 5. The ETM+ also includes

new features that make it a more versatile and efficient instrument for global

19

 

change studies, land cover monitoring and assessment, and large area mapping

than its design forebears [25]. The primary new features on Landsat 7 are:

• a panchromatic band with 15m spatial resolution

• on board, full aperture, 5% absolute radiometric calibration

• a thermal IR channel with 60m spatial resolution

Table (2-3) shows the characteristics of Landsat 1 to 7 missions, and table

(2-4) shows current remote sensing systems and their major characteristics.

20

 

Landsat 1-3 Multispectral scanner

Landsat 4-5 Thematic Mapper

Landsat 7 Enhanced Thematic Mapper+

Operation 1972-1982 since 1982 since 1999

Altitude 915 Km 705 Km 705 Km

Revisit Period 18 days 16 days 16 days

Swath Width 185 Km 185 Km 185 Km

Ground Resolution 79 × 79 m2 30 × 30 m2 30 × 30 m2

Spectral Bands

1 = 0.45 - 0.52 µm 1 = 0.45 - 0.52 µm BLUE (B)

4 = 0.50 - 0.60 µm 2 = 0.52 - 0.60 µm 2 = 0.52 - 0.60 µm GREEN (G)

5 = 0.60 - 0.70 µm 3 = 0.63 - 0.69 µm 3 = 0.63 - 0.69 µm RED (R)

6 = 0.70 - 0.80 µm 4 = 0.76 - 0.90 µm 4 = 0.76 - 0.90 µm NEAR INFRARED

(NIR) 7 = 0.80 - 1.10 µm

5 = 1.55 - 1.73 µm 5 = 1.55 - 1.73 µmMIDDLE

INFRARED (SWIR1)

7 = 2.08 - 2.35 µm 7 = 2.08 - 2.35 µmMIDDLE

INFRARED (SWIR2)

8 = 10.4 - 12.5 µm (237 × 237 m2)

6 = 10.4 - 12.5 µm (120 × 120 m2)

6 = 10.4 - 12.5 µm (60 × 60 m2)

THERMAL INFRARED

(TIR)

8 = 0.52 - 0.9 µm (15 × 15 m2)

PANCHROMATIC(Pan.)

Table (2-3): Characteristics of Landsat 1 to 7 missions [26]

21

 

Remote Sensing Systems No. of Bands

Spatial Resolution

(meters)

Temporal Resolution

(days) GOES Series (east

and west) 4, Panchromatic 700 0.5/hr

SeaWiFS 8 1130 1

ASTER 14 15-30-90 5-16

NOAA-9 AVHRR LAC 5 1100 14.5/day

NOAA- K, L, M 6 1100 14.5/day

ATLAS 14 2.5 to 25 Variable

AVIRIS 224 2.5 or 20 Variable

OMEGA 351 300-5000 Variable

Table (2-4): Selected current remote sensing systems and their major characteristics [18]

22

 

2.6.5 Characteristics of ETM+ Spectral Bands

Landsat 7 is new generation of Landsat satellite that replaces the Landsat

5. The functions of each band in the sensor to the wavelength like ETM+ are

follows [27]:

1. Band 1 (0.45μm-0.52μm), the energy that receive by the band can make a

deep penetration to water column and use to support the land use characteristic

analysis, soil and vegetation.

2. Band 2 (0.52μm -0.60μm), especially are built to sense the top reflection of

vegetation in green spectrum that situated in the two chlorophyll absorption

spectral channels. The observation in this band is means to notice the vegetation

differences and the fertilization level grading.

3. Band 3 (0.63μm -0.69μm), are an important channel to separate vegetation.

The channel is in one part of the chlorophyll absorption and strengthens the

visible contras between vegetation and not vegetation, and also to sharpen the

contras in vegetation class.

4. Band 4 (0.76μm -0.90μm), are plan to be perceptive to the amount of

vegetation. This can help identify plant and can strengthen contras between

plant-soil and land-water.

5. Band 5 (1.55μm-1.73μm), are the important channel in identifying plant

species, water resistance in plant and the soil humidity condition.

6. Band 6 (10.40 μm-12.50μm), are important channel to observed the earth

heat surface.

7. Band 7 (2.08μm-2.35μm), are an infrared channel that are known to classify

and analyze vegetation, soil humidity separation and other effect that are

involve with heat.

23

 

8. Band 8 (0.52 μm-0.9μm), include portion of the visible and the near infrared

bands [27].

CHAPTER THREE

BANDS TRANSFORMATION TECHNIQUES &

CLASSIFICATION

 

24  

3.1 Introduction

The multispectral nature of most remote sensing data makes it possible

for spectral transformations to generate new sets of image components or

bands. The transformed image may make evident features not discernable in the

original data or alternatively, it might possibly preserve the essential

information content of the image with a reduced number of the transformed

dimensions [28].

In the context of image processing, the term feature extraction (or feature

selection) are not only geographical features, visible on an image but, rather,

statistical characteristics of image data-individual bands, or combinations of

band values, that carry information concerning systematic variation within the

scene [19].

3.2 Multispectral Remote Sensing Images

Digital images are representation of an object obtained by digital sensor

device. The sensor records digital numbers (DNs) which are related to a

property of the object such as its reflectance. Digital remotely sensed image is

typically composed of picture elements (pixels) [29].

Satellite and airborne multispectral sensors provide data in the form of

several images of the same area of the Earth’s surface, but taken through

different spectral windows or bands [5]. A band wavelength range measured by

a remote sensing system often used to designate how many layers of data are

recorded by a system, e.g. Landsat TM is a seven band system. The term

25  

channel is occasionally used instead of band. Multispectral imagery provides

more information that data collected in any single spectral band image.

Digital Number value DN assigned to a pixel which is related to the

parameter being measured by a remote sensing system, a low value of DN may

represent a low reflectance in particular waveband while high value of DN

represent a high reflectance. DN values must be integers and common values

between 0-255, for an n-band dataset each pixel is associated with n digital

numbers [16].

A significant advantage of multispectral imagery is the ability to detect

important differences between surface materials by combination spectral bands.

Different materials may appear virtually the same within a single band [30].

Color images can be modeled as three band monochrome image data,

where each band of data corresponds to a different color. The actual

information stored in the digital image data is the brightness value in each

spectral band. Whenever the image is displayed, the corresponding brightness

information is displayed on the screen by picture elements that emit light

energy corresponding to that particular color.

Typical color images are represented as red, green, and blue, a display of

three band image in which one band is applied to the red region, one to the

green and one to the blue, it called RGB display [31].

3.3 Band Ratios

Each object has its own spectral reflectance pattern in different wavelength

portion. Spectral reflectance curve is a kind of fingerprint of the object. The

objects may have high reflectance value in some spectral portion, however, it

26  

may absorb in another spectral region. According to this information, the main

concept of the band ratios technique was developed [32].

A band ratio is very simple and powerful technique in the remote sensing.

Basic idea of this technique is to emphasize or exaggerate the anomaly of the

target object [32].

Ratio is accomplished by dividing the data base DNs in one spectral band,

by the data base DNs in a second spectral band for each spatially registered

pixel pair. The ratio algorithm can be expressed in the form [3]:

DNo , =(DN ,

DN ,) (3-1)

Where: DNo(j,s)= output digital number at line j, sample s,

DNx(j,s)=input digital number of band x at line j, sample s,

DNy(j,s)=input digital number of band y at line j, sample s.

On band ratios image the extreme black-and-white tones of the gray scale

represent the maximum difference in spectral reflectivity between two bands.

The darkest tones are targets for which denominator of the ratio is greater than

the numerator. Conversely the numerator is greater than the denominator for the

lightest tones [17].

Ratio techniques are usually used to enhance the spectral differences

between surface materials that difficult to detect in raw images. Moreover, such

techniques may suppress the effect of variable illumination resulting from

topographic variations, slope shadows, seasonal changes, and either differences

in sunlight angle or intensity may also be eliminated. Information gained by

27  

ratio transforms is considered to be almost new and cannot be obtained from

either of the bands independently [33].

Every pixel is one of the commonest arithmetic operations performed on

digital remote sensing data. If any area of forest-land on either side of a hill is

imaged in two bands, see figure (3-1), it will not have a uniform signature on

either image because the digital numbers will be consistently lower for both

bands in shadow. So classification of this scene may correspondingly assign

this part to different classes even though they should be considered as

belonging to a single class thus rationing effectively suppresses the topographic

effect and enhances gradient changes in the spectral reflection and reflectivity

curves of different materials, which may accentuate the difference between

them. This example was chosen deliberately because ratioing is particularly

relevant to vegetation studies [20].

Figure (3-1): Reduction of scene illumination effects through spectral ratioing [20]

28  

The utility of any given spectral ratio depends upon the particular

reflectance characteristics of the features involved and the application at hand.

The number of possible ratios that can be developed from n bands of the data is

n(n-1). Thus, for the six non thermal bands of Landsat TM or ETM+ data there

are 6(6-1), or 30, possible combinations [20].

Ratio images can often provide information that cannot be obtained from

the band singly; there may be occasions where ratio image provides less

information. Thus, ratioing may result in two surfaces that could be

distinguished on two separates bands being indistinguishable on a ratio image.

For explanation, two surfaces have been imaged by two bands as follows:

Bands (1) DN (1) =60 DN (1) =80

Bands (2) DN (2) =30 DN (2) =40

For the above example the two surfaces can distinguished on either band

(1) or band (2), where in both cases surface B has a higher DN and will

consequently appear pale (brighter). However both surfaces have the same ratio

value =2 and would be indistinguishable on a ratio image of bands 1 and 2 [21].

Ratio=2 Ratio=2

Surface A                          Surface B                           

29  

3.4 Vegetation Indices

Vegetation indices are quantitative measures, based on digital values that

attempt to measure biomass or vegetation vigor. Usually a vegetation index is

formed from combinations of several spectral values that are added, divided, or

multiplied in a manner designed to yield a single value that indicates the

amount or vigor of vegetation within a pixel. High values of the vegetation

index identify pixels covered by substantial proportions of healthy vegetation.

The simplest form of vegetation index is simply a ratio between digital values

from separate spectral bands [19].

The green vegetation would have slightly higher reflectance in the green

portion of the visible spectrum than in blue or the red. The dips in the blue and

the red are due to photosynthetic absorption at those wavelengths. To take

advantage of this particular spectral feature, researchers have developed a

metric known as the Normalized Difference Vegetation Index (NDVI) which is

equal to the signal received in the near infrared minus the signal in the red

divided by the sum of the signals in those bands. (This normalization is

performed to reduce intra-class variation due to lighting and topographic

effects) [34].

NDVI NIR RNIR R

(3-2)

Where NIR: Near Infrared band (0.76 - 0.90 µm),

R: Red band (0.63 - 0.69 µm).

30  

This index largely compensates for changing illumination conditions,

surface slope, and viewing aspects. Healthy vegetation reflects strongly in the

near infrared portion of the spectrum while absorbing strongly in the visible red.

Other surface types, such as soil and water, show near equal reflectance in both

near infrared and red portions. Thus, the NDVI image would significantly

enhance the discrimination of vegetation from other surface cover types.

In general, vegetation yields high positive NDVI values. Clouds, water,

and snow yield negative values due to larger red reflectance than near infrared.

The NDVI values for rock and bare soil are near zero due to their similar

reflectance in both bands. Therefore, in a NDVI image the lighter tones are

associated with dense coverage of healthy vegetation.

3.5 Spectral Indices

There are other indices which are used in environmental monitoring and

image enhancement, there as follow:

1. Bare Soil Index (BSI) Bare Soil Index used to identify the bare soils which include bare area,

where [12]:

BSI SWIR R _ NIR BSWIR R NIR B

+1 (3-3)

Where SWIR1: is Short Wave Infrared one (1.55 - 1.73 µm),

B: is blue band (0.45 - 0.52 µm),

R: is Red band (0.63 - 0.69 µm).

31  

2. Normalized Differential Water Index (NDWI) This index is used to oversee the situation of water in the study area. The

ratio between red and SWIR1 spectral region dearly enhanced water bodies to

the brighter pixels, where [12]:

NDWI R SWIRR SWIR

1 (3-4)

3. Urban Index (UI) At Most enhanced the urban activities such as housing, road, industrial

complex and so on, where [12]:

UI SWIR RSWIR R

1 (3-5)

Where SWIR2: Short Wave Infrared two (2.08 - 2.35 µm).

Originally all these equations produce relative value ranges from -1 to +1.

Number 1 has been added in each equation to avoid the negative value in

further analysis. Therefore, the entire resulted images will have the value

between 0-2, where higher value represents better existences of the selected

environmental parameters (BSI, NDWI, and UI) [12].

3.6 Separating Soil Reflectance from Vegetation Reflectance

In agricultural scene, however, reflections from individual plants, or

individual rows of plants, are closely intermingled with the bare soil between

plants and between rows of plants, so that reflectance are mixed even at the

32  

finest resolution. But even after leaves have fully emerged, soil can still

contribute to reflectance because of penetration of some wavelengths through

the vegetation canopy.

In many spectral observations are made of surfaces of bare soil, the values

of red and near infrared brightness resemble those in figure (3-2) [20]. The soil

brightness tends to fall on a straight line as a soil becomes brighter in the near

infrared, so does it tend to get brighter in the red. Dry soils tend to be brighter in

both spectral regions and appear at the high end of the line C; wet soils tend to

be dark, and are positioned at the low end B.

In 1977 Richardson and Wiegand defined this relationship, known as the

soil brightness line, and recognized that spectral response of living vegetation

will always have a consistent relationship to the line. Soils typically have high

or modest response in the red and infrared regions, whereas living vegetation

must display low values the red (due to the absorption spectra of chlorophyll)

and high values in near infrared.

Figure (3-2): Soil brightness line [19]

33  

Thus, point X typifies a “pure” vegetation pixel, with a spectral response

determined by vegetation alone, with no spectral contribution from soil. In

contrast, point Y typifies a response from a partially vegetation pixel, it is

brighter in the red and darker in the near infrared than is X [19].

3.7 Tasseled Cap Transformation

In 1976, Kauth and Thomas produced a transformation of the original

Landsat MSS data space to a new four dimensional spectral space. It was called

the Tasseled Cap or Kauth-Thomas transformation. The transformation consists

of linear combinations of the four MSS bands to produce a set of four new

variables [18]:

TC1=+0.332MSS1+0.603MSS2+0.675MSS3+0.262MSS4 (3-6)

TC2=-0.283MSS1-0.66MSS2+0.577MSS3+0.388MSS4 (3-7)

TC3=-0.899MSS1+o.428MSS2+0.076MSS3-0.041MSS4 (3-8)

TC4=-0.016MSS1+0.131MSS2-0.452MSS3+0.882MSS4 (3-9)

These new bands are designated as, for example, “TC1”, “for Tasseled

Cap band 1”. Although these four new bands do not match directly to

observable spectral bands, they do carry specific information concerning

agricultural scenes [19]. It can therefore in corporate more information than

ratio or modified ratio indices (like most vegetation indices) [35].

TC1 interpreted as brightness, a weighted sum of all four bands. TC2 is

designated as greenness, a band that conveys information concerning the

34  

abundance and vigor of living vegetation. TC3 depicts yellowness, derived

from the contrast between red and green bands. Finally, TC4 is referred to as

nonsuch because it cannot be clearly matched to observable land space features

and is likely to carry system noise and atmospheric information.

The first two bands (TC1 and TC2) usually convey almost the information

present in an agricultural scene often 95% or more. Therefore, the essential

components of an agricultural landscape are conveyed by a two-dimensional

diagram, using TC1 and TC2 figure (3-3) [19].

Figure (3-3) shows a plot of data which is physically-based on crop

growth. The growing cycle of crop started from bare soil, then to green

vegetation and then to crop maturation with crops turning yellow. This different

stage of vegetation growth has made the data distribution in the two

Figure (3-3): Seasonal variation of a field in defined by Greenness and Brightness [19]

35  

dimensional multispectral space as shown in figure (3-4) which appear in a

shape of cap [28].

The Tasseled Cap transformation on six non-thermal bands of Landsat TM

or ETM+ data can be performed using the following formulas [19]:

Brightness = 0.3037(TM1) + 0.2793(TM2) + 0.4743(TM3)

+ 0.5585(TM4) + 0.5082(TM5) + 0.1863(TM7) (3-10)

Greenness = -0.2848(TM1) - 0.2435(TM2) - 0.5436(TM3)

+ 0.7243(TM4) + 0.0840(TM5) - 0.1800(TM7) (3-11)

Wetness = 0.1509(TM1) + 0.1973(TM2) + 0.3279(TM3)

+ 0.3406(TM4) - 0.7112(TM5) - 0.4572(TM7) (3-12)

Figure (3-4): 2D scatter plot of two Tasseled Cap components [35]

36  

3.8 Principle Component Analysis (PCA)

In satellite imagery, it is not uncommon to find that a strong degree of

correlation exists between the multi-spectral bands. Such correlation indicates

that if reflectance is high at a particular location on one band, they are also

likely to be high on the other band. In the extreme case, if two bands were

perfectly correlated they would essentially describe the same information. It is

not unusual to find that an image with 7 bands, such as Landsat Thematic

Mapper, actually contains far fewer than 7 bands of true information.

Principal Components Analysis (PCA) can be used to transform a set of

image bands such that the new bands (called components) are uncorrelated with

one another and are ordered in terms of the amount of image variation they can

explain. The components are thus a statistical abstraction of the variability

inherent in the original band set. Since each of the components produced by this

transformation is uncorrelated with the other, each carries new information.

Also, because they are ordered in terms of the amount of information they

carry, the first few components will tend to carry most of the real information in

the original band set, while the later components will describe only minor

variations [36].

The techniques of PCs analysis which provides means of generating set of

m-images with particular properties (zero correlation between m-bands and

maximum variance) from set of m-correlated images. It is usually found that, as

a result of the maximum variance property, of the PCs information in m

correlated bands is expressible in term of P (PCs), where p is less than m [37].

37  

 

PCA has several advantages, they are [38]:

1. Most of the variance in a multispectral data set is compressed. The

information contents of a number of bands, for example, (six TM bands) into

just two or three transformed PC images. The transformation provides

uncorrelated variables with the first two or three components generally

containing (90 to 97) percent of the variance of the original six TM bands.

2. Noise may be relegated to the less correlated PC images.

3. Spectral differences between materials may be more apparent in PC images

than in individual bands.

However the PCA can be used for the following applications [2]:

• Effective classification of land use with multispectral data.

• Color representation or visual interpretation with multispectral data.

• Change detection with multi-temporal data.

• Compression of multispectral data.

Figure (3-5): PCA reduce the dimensionality

38  

3.8.1 Mathematical Representation [5]

Let us consider two dimensional image represented by an array:

(3-13)

And the mean is defined by

(3-14)

Where is the expectation value. So the covariance matrix could be

defined as follow:

(3-15)

(3-16)

Where T indicates to the transpose.

The diagonal elements are the variance of each image, i.e. .

where is the standard deviation. The eigenvector and eigenvalue of the

covariance matrix denoted by and respectively.

39  

The transformation matrix consists of the eigenvector of the covariance

matrix:

(3-17)

Where is unity matrix such that . In order to compute the

principle component we should diagonalized the covariance matrix using the

following expression:

0 00 0 0 0

(3-18)

So each eigenvalue corresponds to the variance of a new PC image, the

variance being related to the amount of contrast. The new matrix is diagonal

and the components have been chosen to be uncorrelated and

.

 

40  

3.9 False Color Composite (FCC)

Combining data from a number of bands into color images allows more

information to be extracted. The image must be displayed in those colors

irrespective of the wavelength at which the image was obtained, (the human eye

responds to radiation between wavelength of approximately 0.38 and 0.72) [39].

The aims of false color composites are to [16]:

• Discriminate different bands combination to yield information on

different surfaces and in a single image.

• Investigate how surface appears different in visible, infrared and

microwave electromagnetic spectrum.

The large number of bands for the ETM+ system allows the production of

much great numbers of false color composite.

3.9.1 Selecting the Best Color Composites

The number of different color composites that can be produced by

combining single band black-and-white images in groups of three (red, green,

and blue components) can be easily determined for any multispectral imaging

system by use of the following equation [3]:

N !! !

(3-19)

Where: N=number of different color composites,

n=number of single band images available,

3=number of color assignments (red, green, and blue components).

41  

Selecting the three band combination for color compositing that will allow

for the optimum discrimination of material classes in a scene can be both

difficult and time consuming because of the large number of possible

combinations. For example there are 20 different three band color composites

produced from the six non thermal ETM+ images [3]. To help overcome the

selection problem, various quantitative criteria have been developed to assist in

selecting which band combinations to include in color composites. The

Optimum Index Factor (OIF) is one such criterion. It ranks all possible three

band combinations based on the total variance present in each band and the

degree of correlation between bands [20]. High OIF values indicate bands that

contain much information (e.g. high standard deviation) with little duplication

(e.g. low correlation between the bands). By using the OIF technique, three

band color composites can be evaluated on their effectiveness for display. OIF

calculated using the following equation [14]:

OIF ∑∑ | | (3-20)

Where σ(i) is the standard deviation of band i, and r(j) is the correlation

coefficient between any two bands in the combination[24].

In this project the OIF factor technique has been adopted on the original

ETM+ bands and on the bands ratios to select best three bands combination for

classification.

42  

3.10 Multispectral Classification

In remote sensing data provide the most significant clues about what is on

the ground, the spectral data in a scene can be recorded with multispectral

images. Each pixel in a multispectral image has spatial coordinates x & y and

spectral coordinates λ. It has characterized by its spectral signature, which is

determined by the relative reflectance in different wavelength bands.

Multispectral classification is a data extraction process that analyzes these

spectral signatures then assigns pixels to categories based on similar signatures

[40]. Pixels that do not fall within spectral class (cluster) are considered

unclassified.

The results from the classification process are typically in the form of a

thematic map from which data can be used to solve a particular problem or to

provide important data unobtainable from other sources.

There are two major approaches to multispectral classification of remote

sensing data [41]:

• Unsupervised classification technique (clustering).

• Supervised classification technique (human assisted).

3.10.1 Unsupervised Classification

Unsupervised classifiers do not utilize training data as the basis for

classification. Rather, this family of classifiers involves algorithms that examine

the unknown pixel in an image and aggregate those into a number of classes

based on natural grouping or clusters presented in the image values. This

method is called unsupervised classification because the data analyst has little

43  

control over the establishment of the decision regions, so it is more computers

automated. It allows specifying some parameters, which the computer uses to

uncover statistical pattern that are inherent in the data [20]. Clustering

algorithms are used for unsupervised classification of remotely sensed data.

Many clustering algorithms have been developed for a wide variety of purposes

methods of unsupervised classification attempt to find clusters in the

distribution of the pixels [42].

The aim of clustering methods is to partition a set of data points into a

given number of clusters.

Clustering algorithms used for the unsupervised classification of remotely

sensed data generally vary according to the efficiency with which the clustering

takes place [29].

The advantages of clustering algorithm:

• No pre-knowledge required.

• Suitable for the application with large database.

There are more common methods of the determination of the clusters in the

data such as K-means algorithm, and Iterative Self Organizing Data Analysis

(ISODATA) algorithm. The first step of ISODATA algorithm, an initial mean

vector is arbitrarily specified for each class. Each pixel in the scene is assigned

to the class whose mean vector is closest to the pixel vector forming the first set

of decision boundaries. A new set of class mean vectors is then calculated from

the results of the previous classification and the pixels are resigned to the

classes. The procedure continues until there is no significant change in pixel

assignment from iteration to the next [40].

44  

3.10.2 Supervised Classification

Supervised classification can be defined informally as the process of

using samples of known identity (i.e. pixels already assigned to informational

classes) to classify pixels of unknown identity (i.e. to assign unclassified pixels

to one of several informational classes) samples of known identity are those

pixels located within training areas, the selection of these training data is a key

step in supervised classification [19].

The classification technique will be as follows:

• Training site selection,

• Supervised classification,

• Output stage,

• Accuracy assessment.

a. Training Stage

Training area is an area on an image which the operator, using prior

knowledge, knows it belongs to one specific class which will be used in a

supervised classification procedure; it is considered the first step of any

classification procedure to train the computer program to recognize the class

signature of interest. The main problem is the selection of real representative

sample areas for the selected classes [16].

The selecting of training data has the most effect on the accuracy of the

classification results. Training stage requires close interaction between the

image analyst and image data, it also requires substantial reference data,

45  

therefore, the analyst uses prior knowledge, derived from field surveys, photo

interpretation and other sources about small region of the image, to be classified

to identify these pixels that belong to the classes of interest [41].

An important statistical step of selecting training data is that, a sufficient

number of pixels may be needed to define the class signature properties

accurately [20]. Sometimes, to obtain a good estimation of classes statistics it

may be necessary to choose several training fields for the one cover type,

located in different regions of the image, a sufficient number of pixels must be

used to define a class signature propertied accurately [41].

b. Classification Stage

Supervised classification is the procedure most often used for extraction

of quantitative information of remote sensing image data; it rests upon using

suitable algorithm to label the pixels in an image representing particular cover

type, or class.

Many classification methods are available such as Minimum Distance to

Mean classifier, the Parallelepiped classifier and the Maximum Likelihood

classifier. The Maximum Likelihood procedure is unquestionably the most

commonly used procedure for classification in remote sensing. The foundation

for this approach expresses the relationship between evidence, prior Knowledge

and the likelihood that a specific hypothesis is true [36]. In this method the

computer calculates the probability of a pixel value occurring in the distribution

of the class to be a member of a particular of a certain class. The class with

maximum probability is recorded as the correct class of that pixel. After

evaluating the probability in each category, the pixel would be assigned to the

46  

most likely class (highest probability value) or be labeled “unknown”. The

Maximum Likelihood classification is the most accurate in results; its accuracy

depends on the skill in selection of training areas [20]. The equation for the

Maximum Likelihood classifier is as follow [39]:

log log |∑ | ∑ (3-21)

Where:

= Weighted distance (Likelihood),

= Data vector,

= The mean vector of the sample of class (i),

∑ = The covariance matrix,

|∑ |= Determinate of ∑ , = percent probability that any pixel is a member of class (i),

log = natural logarithm function.

c. Output Stage

The results of image classification can be introduced in several forms.

The three general forms are [43]:

• Tabular data: These are tables listing necessary statistics of the whole

image or any desired part.

• Graphical products: Display of classified image (the classes) in different

colors, tones or characters, each cell represents its related ground cover

classes.

47  

• Digital information files: Output results may be recorded on computer

disks for further analysis and processing.

d. Accuracy Assessment

As the classification process is preformed, there must be some errors. To

correctly performed classification accuracy assessments, it is necessary to

compare two sources of information: (1) the remote-sensing-derived

classification map and (2) what we will call reference test information (which

may in fact contain error). The relationship between these two sets of

information is commonly summarized in an error matrix.

Accuracy assessment of the classification represented by error matrix,

which is a square array of numbers laid out in row and columns that express the

number of sample units assigned to a particular category relative to the actual

category as verified in the field. The columns normally represent the reference

data, while the rows indicate the classification generated from the remotely

sensed data. An error matrix is a very effective way to represent accuracy

because the accuracy of each category is clearly described [29].

The overall classification accuracy can be calculated using the equation

[44]:

P(%)= ∑ N∑ T

*(100%) (3-22)

Where:

∑ N = the total number of correctly classified pixels in all classes.

∑ T = the total number of test pixels in all classes.

CHAPTER FOUR

RESULTS & DISCUSSION

 

48  

 

4.1 Introduction

The results of band transformation techniques and image classification of

the study area are illustrated in this chapter. All images are adopted by using

ENVI version 4.1, a digital image processing software package, for more detail

see the appendix. The type of data (images) which are used in transformation

techniques is 6 ETM+ band (non-thermal band) images acquired in 15/9/2002.

Figure (4-1) shows the 6 band images, with their histograms, and figure (4-2)

shows the scheme of the work.

 

 

 

 

 

 

 

 

 

 

 

 

 

Band 1

Band 2

Figure (4-1): 6 non thermal ETM+ band images with their histograms (continue)

49  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Band 3

Band 4

Band 5

Band 7

Figure (4-1): 6 non thermal ETM+ band images with their histograms

50  

 

Raw data (6 nonthermal ETM+ satellite images acquired in 15/9/2002)

Tasseled Cap Band Ratios PCA

30 different ratios

Visual interpretation

5/7, 4/5, 2/5, 1/4, 2/4

Optimum Index Factor

False Color Composites

4/5-1/4-2/5

Classification

Supervised Classification

Accuracy

False Color Composites False Color Composites

Optimum Index Factor

3-4-7

False Color Composites

Figure (4-2): The scheme of the work

Unsupervised Classification

51  

4.2 Band Ratios

Remotely sensed data acquired from high altitude platforms have

strongly developed during the last decades and became a very important tool for

many applications. Image enhancements are the techniques by which the image

becomes more informative. Band ratios represent one of the spectral

enhancement techniques which represent the ratio between digital numbers for

two or more different bands. The whole information that content in

multispectral images gives an opportunity to use such technique.

The number of possible ratios has been computed using n (n-1); where n

is the number of spectral bands, so there are 30 different ratios, 15 original and

15 reciprocal. Our goal is focused on getting the best ratios for land cover and

land use features (water, vegetations, bare lands, crop lands, and urban areas),

this done by visual interpretation for the 30 different ratios.

The images contain detailed record of features on the ground at the time of

data acquisition. The images have been examined, and frequently, other

supporting material such as maps and reports of the field observations. In this

work, visual interpretation is made as to the physical nature of features and how

much details of this features appeared in the 30 different ratios. According to

this, five different ratios have been chosen to be the best ratios represent land

features based on visual interpretation. These ratios are given in table (4-1).

52  

Land Feature Band Ratio

Water 5/7

Vegetations 4/5

Bare lands 2/5

Urban areas 1/4

Crop lands 2/4

The general characteristics of major land cover and land use features that

have been seen on the five ratios images are discussed below:

1. Water:

The best ratio which identifies water is 5/7 (SWIR1/SWIR2). The

existence of water in different locations of study area, it includes Tigris River

and the canals that came from it. Water appeared as black in this ratio, this due

to the low reflectance of it in the IR bands. The small canals were merged to

the other land features because the limitation of the resolution of the image,

figure (4-3).

Table (4-1): The best band ratios for certain features

53  

2. Vegetations:

Healthy vegetations have high reflectance in the range of infrared

wavelength, and low in the range of visible red wavelength. The best ratio

which identified vegetations in study area is 4/5 (NIR/SWIR1), where they

appeared as bright area due to the high reflectance at NIR band, figure (4-4).

Vegetations in study area are represented by agriculture and field lands which

are located at the both sides of the river and canals.

Figure (4-3): 5/7 band ratio for water

54  

3. Bare lands:

The existence of bare lands in study area is out of city which is not used

for residence or agricultures and its surface are predominantly thin soils or

sands. The best ratio which identified barren areas is 2/5 (Green/SWIR1), which

its tone appeared as medium gray to little dark tones from other land features as

shown in figure (4-5). The appearance of bare lands is different due to different

surface roughness, content, moisture content and the presence of salts.

Figure (4-4): 4/5 band ratio for vegetations

55  

4. Urban areas:

Urban areas represented the areas of intensive land use where much of

these areas consist of buildings, streets, and highways which connected the

cities. The 1/4 (Blue/NIR) ratio has been adopted to be the best ratio for

identifying the urban areas, which separated this lands from the other features,

and appears as bright tone, see figure (4-6). Some roads are indistinguishable

because the limitation of resolution.

5. Crop lands:

The ratio which identified these lands is 2/4 (Green/NIR), which appears

as lighter gray due to the high reflectance at visible band; figure (4-7).

Figure (4-5): 2/5 band ratio for bare lands

56  

Figure (4-6): 1/4 band ratio for urban areas

Figure (4-7): 2/4 band ratio for crop lands

57  

Tables (4-2) and (4-3) show the basic statistics and correlation of band

ratios respectively.

Bands MIN. MAX. MEAN STD

Band 5/7 0 28.5 0.96 0.61

Band 4/5 0 85 1.44 3.22

Band 1/4 0 36.25 0.82 0.66

Band 2/4 0 29.5 0.84 0.57

Band 2/5 0 58.33 0.81 0.59

Band 5/7 Band 4/5 Band 1/4 Band 2/4 Band 2/5

Band 5/7 1 0.046 0.075 0.071 0.017

Band 4/5 0.046 1 0.190 0.221 0.073

Band 1/4 0.075 0.190 1 0.964 0.211

Band 2/4 0.071 0.221 0.964 1 0.248

Band 2/5 0.017 0.073 0.211 0.248 1

Table (4-2): Multivariate statistics of the band ratios

Table (4-3): Correlation between band ratios

58  

4.3 Tasseled Cap Transformation

Tasseled Cap transformation is one of the available methods for

enhancing spectral information content of Landsat ETM+ data. It attempts to

reduce the amount of data layers (dimensionality) needed for interpretation or

analysis by using mathematical equations to transform the original bands into

new dimensional space.

Tasseled Cap transformation has been adopted on the six non-thermal

ETM+ bands of study area. The production of Tasseled Cap consists of three

main indices: brightness, which contains the most information, greenness,

which conveys information about living vegetation since it displayed areas with

vegetation as the lightest tone (white areas), and wetness which is concern with

surface wetness where the river and canals appear as white as shown in figure

(4-8).

Tasseled Cap transformation showed high ability to improve

interpretability and extract information from the data which is not readily

visible in the raw form of ETM+ data of the study scene.

If the greenness and brightness components of a typical scene are plotted

perpendicular to one another on a graph to display the distribution of the data in

spectral plot, the shape of data plot is look like a cap, so that the name of

Tasseled Cap comes from this fact.

59  

 

Brightness

Greenness

Wetness

Figure (4-8): The first three Tasseled Cap transformation indices

60  

4.4 Vegetation indices

Vegetation indices kind of bands transformation techniques, one of them

is NDVI which is a good tool to transform multispectral data into a single

image band representing vegetation areas. The higher value of NDVI indicate

that the higher probability that the corresponding area on the ground has a dense

coverage of healthy green vegetation. The reflectance of vegetation is different

and depends on the type, situation and the water content of it.

NDVI has been adopted on the raw bands of the study area see figure

(4-9). The white area indicated dense healthy vegetations. The existence of

vegetations in study area is around the river and the canals.

Figure (4-9): NDVI for the study area

61  

There are other indices which are used for feature extraction and

monitoring the land cover and land use, such as Bare Soil Index (BSI),

Normalized Difference Water Index (NDWI), and Urban Index (UI), where

computed in multispectral ETM+ images and tried to show their abilities for

monitoring the environment in respect of bare soil lands, water and urban

activities as shown in figure (4-10).

From figure (4-10) BSI shows the reflectance of bare lands as lighter gray,

this mean that there is high reflectance of bare lands at the 7. The urban areas

appeared as bare lands, this occurred because some urban features have similar

spectral signatures as bare lands and vice versa.

The result of NDWI shows that the river appeared as black tone but the

other canals appeared as white tone. The reflectance of water depends on the

turbidity and the depth of water and reaches its maximum value of reflectance

at the blue end of the spectrum and decreases as wavelength increases.

Unfortunately, the urban areas are undistinguishable in UI image and

appeared as mid gray tone. The appearance of vegetation is good in UI which

appeared as white.

62  

 

BSI NDWI

UI

Figure (4-10): Spectral indices images

63  

4.5 Principle Component Analysis

Principle Component Analysis (PCA) is a spectral enhancement which

can be used to compress the information content of a multispectral data set.

PCA uses mathematical algorithms to transform n bands of correlated data into

principle components which are uncorrelated.

PCA has been applied to a six non-thermal bands of this study. A set of

six principle components where obtained, and they are shown with their

histograms in figure (4-11).

Table (4-4) shows the multivariate statistics of the original bands of the

study area. Band 7 has the largest standard deviation (STD), this indicates that

the variance is large and the information in this band is very high. While band 1

has the smallest standard deviation, this indicates that the information in this

band is little.

Table (4-5) shows the covariance-variance matrix. The result of

this table shows that the largest value of variance is in band 7, and the

smallest is in band 1. This is obvious from table (4-4) for the standard

deviation.

In table (4-6), there are correlations between the bands. The largest

correlation is between band 1 and band 2; this means that there is common

information in these bands, i.e. these correlation values suggest that there is a

substantial amount of redundancy in the information contents among these

bands. The lowest correlation is between band 4 and band 7, this means that the

relation between them is very small, i.e. there is some information in band 7

that are not found in band 4.

64  

Figure (4-11) shows that the first three principle components contain

most of the information in the original bands, and from histograms one can see

that the histogram of PC1 have wide range of brightness values, this mean that

PC1 image contain most of the information of the scene. However, it has been

observed that each principle component image can be used individually to

enhance and interpret the land features, for example, PC1 image shows the

ability to enhance the water regions which appears as black and the bare lands

which appears as light gray tone area, in PC2 image, it has been found to yield

enhanced the vegetation areas which appears as white areas around the river

and the canals and in PC3 image, it has been found that is contain some noise

rather than the previous PC’s, and it is much able to separate the river and the

canals from the other features.

 

 

65  

  

   

PC1

PC2

PC3

Figure (4-11): PCA images with their histograms (continue)

66  

  

 

PC4

PC5

PC6

Figure (4-11): PCA images with their histograms

67  

Bands MIN. MAX. Mean STD

Band 1 0 255 85.10 44.46

Band 2 0 255 88.79 48.58

Band 3 0 255 97.74 51.70

Band 4 0 255 108.72 46.49

Band 5 0 255 102.98 50.66

Band 7 0 255 106.39 53.02

Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

Band 1 1977.55 2131.12 2218.43 1120.73 1971.29 2062.94

Band 2 2131.12 2360.06 2472.43 1330.01 2233.43 2314.54

Band 3 2218.43 2472.43 2673.78 1354.04 2465.58 2558.82

Band 4 1120.73 1330.01 1354.04 2161.73 1373.42 1142.21

Band 5 1971.29 2233.43 2465.58 1373.42 2567.12 2606.89

Band 7 2062.94 2314.54 2558.82 1142.21 2606.89

2812.01

Table (4-4): Multivariate statistics of the original bands

Table (4-5): Covariance-variance matrix

68  

4.6 False Color Composites (FCC)

From the results of band ratios, Tasseled Cap, and PCA can be used to

generate false color composites by combining three monochromatic (black and

white) images as color images.

From the result of Tasseled Cap, there are three monochromatic bands

(Brightness, Greenness, and Wetness). These three images have been used to

create FCC. From the result of PCA, the best three components which hold the

most information about the scene are PC1, PC2, and PC3. These three

components have been also used to create FCC.

Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

Band 1 1 0.986 0.964 0.542 0.874 0.874

Band 2 0.986 1 0.984 0.588 0.907 0.898

Band 3 0.964 0.984 1 0.563 0.941 0.933

Band 4 0.542 0.588 0.563 1 0.583 0.463

Band 5 0.874 0.907 0.941 0.583 1 0.970

Band 7 0.874 0.898 0.933 0.463 0.970 1

Table (4-6): Correlation between bands

69  

The problem has been come from selecting the best original bands and best

ratios combinations which hold the whole information about the scene.

Optimum Index Factor (OIF) has been adopted to solve this problem,

which it ranks all possible combinations based on the total variance present in

each band and the degree of correlations. The high OIF values indicated bands

that contained much information. OIF method has been adopted on the raw

ETM+ band combinations and on the ratios combinations, which interpreted

land cover and land use of the study scene. For the six raw ETM+ bands, there

are 20 different band combinations which are ranked in table (4-7) with their

OIF values. It is found that the combination of ETM+ raw bands 3, 4, and 7

gives the satisfactory highest OIF value. This indicated that there is low

correlation between bands. For the five band ratios there are 10 different color

combinations which are ranked in table (4-8) with their OIF values. High OIF

value is represented by the combination of band ratios 4/5, 1/4, and 2/5.

Figure (4-12) shows the result of the FCC of the adopted raw bands and

the transformed bands, which are hold the most information about the scene and

represented the good tools towards the classification process.

 

70  

OIF

Order of combination of ETM+ bands

77.172 7 4 3

76.586 7 4 1

75.928 7 4 2

74.478 7 5 4

71.321 5 4 3

70.816 5 4 1

70.094 5 4 2

68.923 4 3 1

68.710 4 3 2

65.905 4 2 1

54.852 7 5 2

54.632 7 5 3

54.472 7 5 1

54.447 7 3 2

53.811 7 3 1

53.290 5 3 2

52.932 7 2 1

52.807 5 3 1

51.906 5 2 1

49.313 3 2 1

Table (4-7): Ranked OIF values of raw band combinations

71  

OIF

Order of combination of ratio bands

47.486 2/5 1/4 4/5

44.111 2/5 2/4 4/5

43.337 2/5 4/5 5/7

20.581 1/4 4/5 5/7

17.925 2/4 4/5 5/7

15.926 2/5 1/4 5/7

11.211 2/5 2/4 5/7

8.081 2/4 1/4 4/5

2.272 2/4 1/4 5/7

1.288 2/5 2/4 1/4

 

Table (4-8): Ranked OIF values of ratio bands combinations

72  

 

 

 

 

 

FCC image for raw bands 3, 4, 7 FCC image for ratio bands 4/5, 1/4, 2/5

FCC image for Tasseled Cap indices FCC image for PC

Figure (4-12): FCC images for different combinations

73  

4.7 Images Classification

The classification process based on the information obtained from band

transformation techniques, visual interpretations, FCC images, and OIF

calculations, the classification process applied on:

1. The raw bands combination ranking of OIF calculation (band 3, band 4,

and band 7).

2. The ratio bands combination ranking of OIF calculation (4/5, 1/4, and 2/5).

3. The indices combination of Tasseled Cap transformation (brightness,

greenness, and wetness).

4. The first three components combination of PCA (PC1, PC2, and PC3).

4.7.1 Unsupervised Classification

Unsupervised classification method has been adopted, were similar pixels

are classified according to natural groupings called clusters. This method

considered an attempt of preprocessing to the supervised classification, in order

to get better understanding about the spatial structure of image data.

Unsupervised classification has been performed using the Iterative Self

Organizing Data Technique (ISODATA) with five clusters.

From the resulted unsupervised classification maps, five different classes

have been identified corresponding to: water (blue), vegetations (green), crop

lands (red), urban areas (cyan) and bare lands (yellow). Figure (4-13) shows the

classification maps for different color combinations. The classified images

74  

show good representation of some classes and merges among the other classes,

especially the classified image of the raw combination, where the bare lands

classified as crop lands and some vegetation areas classified as urban areas. The

classification image of PCA combination (PC1, PC2 and PC3) gives a good

representation of the land features of study area, and pure result in the

classification of band ratios combination.

An advantage of unsupervised classification is that no prior knowledge of

the scene is required, so the setup time is much shorter which generally requires

a minimum input from the operator.

75  

 

Classification of raw bands Classification of band ratios

Classification of PCA Classification of Tasseled Cap

Figure (4-13): Unsupervised classification of different images

76  

4.7.2 Supervised Classification

A supervised classification method has been adopted on the images using

Maximum Likelihood classification. The training samples (region of interests)

have been selected and they are based on land cover and terrain. Five different

land cover and land use classes were selected including water, vegetations,

urban areas, crop lands, and bare lands. The collection of data about land cover

and land use classes of interests is fed to the computer as measurement of each

class to set statistical parameters (min., max., mean and STD).

The selection of training areas was repeated several times to overcome

the cases were some cover material could not be classified accurately and also

to avoid as much as possible the overlapping between the classes.

The classified images gave a good representation of the scene rather than

unsupervised classification images. Some errors are occurred in the

classification, where some of the crop lands are classified as bare lands and

some bare areas are classified as urban areas this done in all the four classified

images. Table (4-9) and figure (4-14) show the results of supervised

classification of different band combinations.

77  

 

Classes Raw bands Band ratios Tasseled Cap PCA

Class name Color No. of pixels

Area (km2)

No. of pixels

Area (km2)

No. of pixels

Area (km2)

No. of pixels

Area (km2)

Bare lands 148665 120.75 188184 152.85 154114 125.17 154927 125.84

Urban areas

151521 123.07 85680 69.59 108486 88.11 109535 88.97

Crop lands

125113 101.62 147096 119.47 161421 131.11 166638 135.35

Water 20029 16.26 28511 23.15 17700 14.37 16714 13.58

Vegetations 531136 43.15 48993 39.79 56743 46.08 50650 41.14

Table (4-9): Results of supervised classification of different band combinations

78  

-A-

-B-

Figure (4-14): Supervised classification of

A-raw bands

B-band ratios (continue)

79  

-C-

-D-

Figure (4-14): Supervised classification of

C- Tasseled Cap

D- PCA

80  

4.7.3 Classification Accuracy Assessment

The classification process is not complete until its accuracy is assessed.

The most common means of expressing classification accuracy is a

classification error matrix (sometimes called confusion matrix or contingency

table). Error matrix compares, on category-by-category basis the relationship

between known reference data (ground truth region of interests) and the

corresponding results of an automated classification.

The overall accuracy is calculated by summing the number of pixels

classified correctly and dividing by the total number of pixels. The pixels

classified correctly are found along the diagonal of the confusion matrix table

which lists the number of pixels that were classified into the correct ground

truth class. The total number of pixels is the sum of all the pixels in all the

ground truth classes. The accuracies of each class are illustrated in tables

(4-10a, b, c, and d). From the results of the classification accuracies, we see that

the accuracies of the classification of the transformed bands including band

ratios, Tasseled Cap transformation, and PC’s combinations are 89.45%,

89.26%, and 89.52% respectively, which are greater than the accuracy of the

raw band of ETM+ of the study scene which is equal 87.18%. This means that

the transformation techniques give a good spectral enhancements and feature

extraction of the raw bands of satellite images and show features that

undistinguishable in a raw images and which represent a good tools for

classification and increasing the accuracy.

81  

1 2 3 4 5 Total

Unclassified 0 0 0 0 0 0

Bare lands 2884 101 82 0 1 3068

Urban areas 98 505 32 3 7 645

Crop lands 159 49 507 3 55 773

Water 0 3 0 226 19 248

Vegetations 0 1 2 3 1105 1109

Total 3141 659 621 235 1187 5843

Overall Accuracy = (5227/5843) 89.4575%

1 2 3 4 5 Total

Unclassified 0 0 0 0 0 0

Bare lands 2676 36 42 1 1 2756

Urban areas 243 592 103 7 16 961

Crop lands 222 23 474 0 41 760

Water 0 6 0 224 1 231

Vegetations 0 2 2 3 1128 1135

Total 3141 659 621 235 1187 5843

Overall Accuracy = (5094/5843) 87.1812%

Table (4-10a): Confusion Matrix of raw band image classification

Table (4-10b): Confusion Matrix of band ratios image classification

82  

1 2 3 4 5 Total

Unclassified 0 0 0 0 0 0

Bare lands 2723 43 43 0 0 2809

Urban areas 139 594 30 4 5 772

Crop lands 279 11 547 3 53 893

Water 0 10 0 224 1 235

Vegetations 0 1 1 4 1128 1134

Total 3141 659 621 235 1187 5843

Overall Accuracy = (5216/5843) 89.2692%

1 2 3 4 5 Total

Unclassified 0 0 0 0 0 0

Bare lands 2720 34 37 0 0 2791

Urban areas 133 606 24 2 4 769

Crop lands 288 11 557 3 60 919

Water 0 8 0 226 1 235

Vegetations 0 0 3 4 1122 1129

Total 3141 659 621 235 1187 5843

Overall Accuracy = (5231/5843) 89.5259%

Table (4-10c): Confusion Matrix of Tasseled Cap bands image classification

Table (4-10d): Confusion Matrix of PCA bands image classification

CHAPTER FIVE

CONCLUSIONS & RECOMMENDATIONS

 

83  

5.1 Conclusions

1. Band transformation techniques generated new sets of image components or

bands and made evident features not discernable in the original data, with a

reduced number of the original bands. Band ratios images gives a good

representation for land features, where the best bands ratios of study area are

5/7, 4/5, 2/5, 1/4, and 2/4 for water, vegetations, bare lands, urban areas, and

crop lands respectively.

2. Optimum Index Factor (OIF) represent optimum method in selecting the best

band combination for more information extracted for the study area, and

adopted for raw bands and bands ratios where:

a. 3-4-7 is the raw band combination that has the highest value of OIF

among 20 different raw band combinations.

b. 4/5-1/4-2/5 is the bands ratio combination that has the highest value of

OIF among 10 different bands ratio combinations.

3. Spectral indices such as NDVI, BSI, and NDWI gives good for monitoring

some land features. While UI gives a poor result for the urban area.

4. The results show that the Principle Components and Tasseled Cap

transformations represented good tools for image enhancement, feature

extraction and reducing the number of bands with no loses in information.

5. Digital image classification of the scene has been applied on:

a. raw band combination (3-4-7)

b. ratios band combination (4/5-1/4-2/5)

c. indices combination of Tasseled Cap (Brightness-Greenness-Wetness)

84  

d. PCA combination (PC1-PC2-PC3).

Where the result of classification show that:

a. Unsupervised classification using ISODATA method is a useful technique, to

prepare a primitive map for the scene. The results of this classification showed

good representation of some classes and merges among the other.

b. Supervised classification using Maximum Likelihood method gives good

representation of the classes with overall accuracies 87.18%, 89.45%, 89.26%,

and 89.52% for raw bands, bands ratio, Tasseled Cap, and PCA images

respectively. This indicated that band transformation techniques increase the

accuracy of the classification and extracted the features that are

undistinguishable in raw bands.

5.2 Recommendations

1. Using multi-temporal images for the same scene with the same

transformation techniques to study the changes in the study area.

2. Using spectral indices such as NDVI, BSI, and NDWI …etc, with the

original bands or transformed bands to generate FCC images, and show how the

classification results appeared.

3. Using Hybrid method for combining between bands and for choosing the

type of image for the classification process, where some of topographic detail is

discriminate between certain features by using this method.

REFERENCES

 

85  

References

1. Elachi, Charles (1987), “Introduction to the Physics and Techniques of Remote Sensing”, John Wiely and Sons, New York.

2. AL-Sepahe, A. H. (2002), “Classification of Digital Satellite Images Using Selective Bands and Principle Component Analysis”, M.Sc. Thesis, Al-Nahrain University, Iraq.

3. Avery, T. E. and Berlin, G. L. (1992), “Fundamentals of Remote Sensing and Air photo Interpretation”, 5th ed., Prentice Hall, New Jersey.

4. Fu, Z., Caelli, T., Liu, N. and Robles-Kelly (2007), “Boosted Band Ratio Feature Selection for Hyperspectyral Image Classification”,

http://users.rsise.anu.edu.au/~arobkell/papers/icpr06.pdf

5. Gonzalez, R. C. and Poul, W. R. (1987), “Digital Image Processing”, 2nd ed., Addison-Wesley Publishing Company, Inc.

6. Huang, C., Wylie, B., Yang, L., Homer, C. and Zylstra, G., “Derivation of a Tasseled Cap Transformation Based on Landsat 7 at Satellite Reflectance”,

USGS EROS Data Center http://landcover.usgs.gov/pdf/tasseled.pdf

7. Al-Ani, L. A. (1996), “Classification of Digital Satellite Images”,

Ph.D. Thesis, Al-Nahrain University, Iraq.

8. Frankovich, J. S. (1999), “Unsupervised Classification of Spectrally

Enhanced Landsat TM Data of Midland, MI”,

http://www.earthsensing.com/drs/stud-proj-f99/final.html

9. Abdeen, M. M., Thurmond, A., Abdelsalam, M. and Stern, R. (2002),

“Application of Aster Band-Ratio Images for Geological Mapping in Arid Regions: the Neoproterozoic Allaqi Suture, Egypt”, Remote Sensing (Syria), 14, 19-38.

86  

10. Inzana, J., Kusky, T., Higgs, G. and Tucker, R. (2003), “Supervised Classification of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of cenral Madagascar”, Journal of African Earth Sciences, 37, 59-72.

11. Feeley, K., Gillespie, T. and Terborgh, J. (2005), “The Utility of Spectral Indices from Landsat ETM+ for Measuring the Structure and Composition of Tropical Dry Forests”, BIOTROPICA, 37, 508-519.

12. Thapa, R., Borne, F., Cu, P. and Porphyre, V. (2005), “Environmental change analysis using satellite imageries: case study of Thai Binh province, Vietnam”,

MapAsia 2005, http://pigtrop.cirad.frlen/worldwide/amerique.htm

13. Fadhil, A. M. (2006), “Environmental Change Monitoring by Geoinformation Technology for Baghdad and its Neighboring Areas”,

http://www.GISdevelopment.net/Application/Environment.pdf

14. Buhe, A., Tsuchiya, K., Kaneko, M., Ohtaishi, N. and Halik, M. (2007), “Land cover of oases and forest in XinJiang, China retrieved from ASTER data”, Advances in Space Research, 39(1), 39-45.

15. Free enecyclopedia (Kut), http://en.wikipedia.org/wiki/Kut

16. Gibson, P. J. and Power, C. H. (2000), “Introductory Remote Sensing: Digital Image Processing and Applications”, Routledge, Taylor and Francis Group, London.

17. Sabins, F. F. (1978), “Remote Sensing Principles and Interpretation”, 1st ed., Remote Sensing Enterprise, Inc., New York.

18. Jensen, J. R. (2000), “Remote Sensing of the Environment An Earth Resource Perspective”, Prentice Hall, New Jersey.

19. Campbell, J. B. (1996), “Introduction to Remote Sensing”, 2nd ed., Taylor and Francis, New York

87  

20. Lillesand, T. M. and Kiefer, R. W. (2000), “Remote Sensing and Image Interpretation”, 4th ed., John Wiley and Sons, New York.

21. Abdul Latiff, R. K. (2005), “Change Detection of Environmental system using satellite images in Shatt AL-Arab region”, M.SC. Thesis, University of Technology, Iraq.

22. Bakker, W. H., Gieske, A. S., and Gort, B. G. (2000), “Principles of

Remote Sensing”, Longman, London.

23. Landsat History (2006), Landsat Newsletter, USGS 2006.

24. LANDSAT 7 +ETM Satellite Imagery,

http://www.satimagecrop.com/satellite-sensors.html

25. Landsat 7, http://geo.arc.nasa.gov/sge/landsat/17.html

26. Description of Landsat data,

http://www.eduspace.esa.int/eduspace/main.asp

27. Winarso, G., Judijanto, and Budhiman, S. (2001), “The Potential

Application Remote Sensing Data for Coastal Study”, Paper presented at

22nd Asian Conference on Remote Sensing, 5-9 Nov. 2001, Singapore.

28. Remote Sensing and Image Analysis,

http://www.cnr.berkeley.edu/~gong/textbook/chapter6/html/sect64.htm

29. Jensen, J. R. (1986), “Introductory Digital Image Processing, A Remote

Sensing Perspective”, Prentice Hall, Englewood Cliffs, New Jersey.

30. Yousif, B. F. (2004), “The Use of Remote Sensing Techniques in the

Classification of Al-Najaf Soil”, M.SC., Thesis, University of Technology,

Iraq.

31. Niblak, W. (1986), “An Introduction to Digital Image Processing”,

Prentice Hall International.

88  

32. San, B. T., Sumer, E. O., and Gurcay, B. (2001), “Comparison of

Band Ratioing and Spectral Indices Methods for Detecting Alunite and

Kaolinite Minerals Using ASTER Data in Biga Region, Turkey”, General

Directorate of Mineral Research and Exploration, RS Center.

33. Mohammad, M., El-Sobky, H., Sedeik, K., and El-Raey, M. (2001),

“Application of Band Ratios Identified by HHRR for Recognition of Surface

Units Using TM Data in SIWA Depression, Western Desert, Egypt”,

Paper presented at 22nd Asian Conference on Remote Sensing, 5-9 Nov.

2001, Singapore.

34. Hultgren, T. L. (2005), “The Role of Resolution in Dasymetric

Population Mapping”, M.A., Thesis, University of Colorado.

35. Paris, J. F. (2005), “Generalized Mapping of Biophysical Properties”,

http://www.microimages.com/freestuf/ScriptsByJack.htm

36. Eastman, J. R. (2001), “Idrisi32 Release 2 Tutorial”, Clark Labs, Clark

University, USA.

37. Ali, A. H. (1998), “Studying the Flooded Area by Principle Component

Analysis of Multi-Temporal Landsat Thematic Mapper Data”, M.Sc.,

Thesis, Al-Nahrain University, Iraq.

38. Smith, W. L. (1977), “Remote Sensing Applications for Mineral Exploration”, Dowden, Hutchinson & Ross. 39. Swain, P. H., and Davis, S. M. (1978), “Remote Sensing: The

Quantitative Approach”, McGrew-Hall, Inc., New York.

40. Schowengerdt, R. A. (1983), “Techniques for Image Processing and

Classification in Remote Sensing”, Academic Press, Inc., New York.

41. Richards, J. A. & Jia, X. (1999), “Remote Sensing Digital Image

Analysis”, Springer-Verlag, New York.

89  

42. Sania, S. A. (2000), “Image Segmentation using Genetic Algorithm”

M.Sc., Thesis, Al-Nahrain University, Iraq.

43. Green, W. B. (1989), “Digital Image Processing: A System Approach”

Van Nostrand Reinhold, New York.

44. Janza, F. J. (1983), “Manual of Remote Sensing”, American Society of

Photogrammetry, Vol. 1, Virginia.

APPENDIX  

90  

Appendix

ENVI (the Environment for Visualizing Images) is a revolutionary

image processing system. From its inception, ENVI was designed to address

the numerous and specific needs of those who regularly use satellite and

aircraft remote sensing data. ENVI provides comprehensive data

visualization and analysis for images of any size and any type all from

within an innovative and user-friendly environment.

ENVI includes all the basic image processing functions within a

friendly, interactive point-and-click graphical user interface. The software

includes essential tools required for image processing across multiple

disciplines, and has the flexibility to allow the user to implement his or her

own analysis strategies. ENVI simplifies comprehensive interactive

processing of large multiband data sets, screen-sized images, spectral plots

and libraries, and image regions-of-interest, all while providing flexible

display capabilities and geographic-based image browsing.

ةـالصـالخ

تحمل معلومات آبيرة عن المناطق قمار الصناعية المتعددة االطياف،صور االولغرض استخالص اآثر آمية ممكنة من المعلومات من تلك الصور، . المراد دراستها

نسب الحزم الطيفية، : استخدمت العديد من تقنيات المعالجة الرقمية، ومن هذه الطرق Principle Component) تحليل المرآبات االساسية، و Tasseled Capتحويل

Analysis) .و تقليل عدد استخالص المعلومات، ان هذه التقنيات تلعب دور مهم في .الحزم الطيفية المستخدمة بدون خسارة في تلك المعلومات

البحث يهدف الى تطبيق عملية التصنيف على نسب الحزم وعلى المعامالت لصور ،Tasseled Capيل المرآبات االساسية و تحويل الصورية المتضمنة تحل

ولبيان آم ،(+ETM)القمر الصناعي من نوع راسم الخرائط الغرضي المحسن  .تختلف دقات هذه التصانيف عن دقة تصنيف الحزم االصلية

ان افضل نسب حزم تم اختيارها لتمثيل خمسة خصائص ارضية النتائج بينت تم تطبيق طريقة الحد االقصى للعامل الكشفي . ٤\٢و ،٤\١ ،٥\٢ ،٥\٤ ،٧\٥هي

(Optimum Index Factor) نسب على مجاميع الصور االصلية و على مجاميع الحيث ،الختيار افضل مجموعة والتي تحتوي على معلومات آبيرة للمنطقة الحزمية،

وافضل مجموعة للنسب ،٧-٤-٣بينت النتائج ان افضل مجموعة للحزم االصلية هي .٥\٢-٤\١- ٥\٤هي

تم اختيارها لتمثيل التصنيف الغير ISODATAطريقة Maximum)وتم اختيار طريقة االحتمالية العظمى ،(unsupervised)موجه

Likelihood) لتمثيل التصنيف الموجه(supervised).

و ،٪٨٩٫٢٦ ،٪٨٩٫٤٥ ،٪٨٧٫١٨ :الدقات الكلية للتصانيف آانت آاالتينتائج و حزم المرآبات ،Tasseled Capحزم للحزم االصلية، نسب الحزم، ٪٨٩٫٥٢ التحويل اعطت تحسينا طيفيا جيدا، ان تقنيات النتائج حيث بينت ،على التوالي االساسية

وتعتبر ن يصعب تمييزها في الصور االصلية،استخالص معلومات آا وعملت على   .ادوات جيدة لزيادة دقة التصنيف

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