hyperspectral signal processing to identify land cover pattern.pdf

8/10/2019 Hyperspectral Signal Processing to Identify Land Cover Pattern.pdf

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VISVESVARAYA TECHNOLOGICAL UNIVERSITY,

Belgaum- 590016,Karnataka

PROJECT REPORT

On

Hyperspectral Signal Processing to Identify Land Cover Pattern

Submitted in partial fulfilment of the requirements for the award of the degree of

Bachelor of Engineering

in

Electrical and Electronics

by

D.R.RAGHURAM 1AR09EE010

MEGHANA SUDHINDRA 1AR09EE026

SUSHANT KULKARNI 1AR09EE044

Under the Guidance of

Internal Guide: External Guide:

Smt R.S.SHARMILA Dr S.N.OMKAR

Assistant Professor Principal Research Scientist

Dept of EEE,AIeMS Dept of AE,IISc

Department of Electrical and Electronics Engineering

AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCES

Near Toyota Kirloskar, Bidadi Industrial Zone, Off Mysore Road, Bangalore - 562109


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Acknowledgements

For the successful completion of this project, many people have taken time out of their busy

schedules and helped us. We would like to acknowledge their help and contribution.

We express our sincere gratitude to Dr A.Prabhakar, Principal, AIeMS and Prof H.L.Dinakar,

Head of Department, Electrical & Electronics Engineering, for encouraging us to carry out

the project at a premier institute.

We are indebted to our internal guide, Smt. Sharmila R.S, Assistant Professor for her

guidance, and constant feedback and support from the very beginning of this project.

We wholeheartedly thank our external guide, Dr. S.N.Omkar, Principal Research Scien-

tist, Department of Aerospace Engineering, Indian Institute of Science for giving us an

opportunity to do our project on such an interesting and upcoming field of research.

We would also like to thank our Project Co-ordinators, Smt. Sulochana Akkalkot, As-

sistant Professorand Ms.Rashmi S, Lecturer, for their valuable suggestions and encourage-

ment.

We are grateful to J.Senthilnath, Doctoral Student, Department of Aerospace Engineer-

ing, Indian Institute of Sciencefor his invaluable guidance and Ashoka Vanjare, Research

Assistant, Department of Aerospace Engineering, Indian Institute of Science for his con-

stant and generous help.We would also like to thankNikil Rao, BITS, Pilani, fellow member

of our project group. We are also thankful to our friends in the Computational Intelligence

Lab, Department of Aerospace Engineering, IISc and all the teaching and non-teaching staff at

our college who have directly or indirectly contributed to the success completion of this project.

Above all, we are thankful to our parents, whose tireless efforts have culminated in us

reaching this juncture of life.


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Contents

Pg.No

List of Figures iii

List of Tables iv

Abstract v

1 Introduction 1

1.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 About hyperspectral signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Geographical area under study . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Outline of the Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Literature Survey 7

3 Image Acquisition 9

3.1 Sensor Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1.1 Scanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1.2 Imaging Optics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.3 Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Mathematical Model of the Instrument. . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Hyperion Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 Spectral Profiles 14

4.1 Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.2 Vegetation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3 Land. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15


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4.4 Urban areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5 Data Preparation 17

5.1 Ground Truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

6 Atmospheric correction 20

6.1 Need for Atmospheric Correction . . . . . . . . . . . . . . . . . . . . . . . . . 20

6.1.1 Reflectance v/s Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6.2 Atmospheric Correction Approaches . . . . . . . . . . . . . . . . . . . . . . . . 22

6.2.1 Scene Based Empirical Approach . . . . . . . . . . . . . . . . . . . . . . 23

6.2.2 Radiation Transport Model Approach . . . . . . . . . . . . . . . . . . . . 23

6.3 QUAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

6.4 FLAASH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6.5 Comparison of the various atmospheric techniques . . . . . . . . . . . . . . . . 26

6.5.1 Explanation of the water spectral profile . . . . . . . . . . . . . . . . . . 27

7 Dimensionality Reduction 29

7.1 Principle Components Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 30

7.1.1 Mathematical Description . . . . . . . . . . . . . . . . . . . . . . . . . . 31

8 Classification Algorithms 35

8.1 Methodology of classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

8.1.1 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

8.1.2 Confusion matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

8.2 Training Data Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

8.3 Spectral Angle Mapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

8.4 Mahalanobis Distance Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . 42

8.4.1 Mathematical Background. . . . . . . . . . . . . . . . . . . . . . . . . . 43

8.5 Minimum Distance Classifier. . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

8.5.1 Mathematical Background. . . . . . . . . . . . . . . . . . . . . . . . . . 45

8.6 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 48


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8.7 K-Means Clustering Classification . . . . . . . . . . . . . . . . . . . . . . . . . 51

8.7.1 Algorithm steps for k-means clustering . . . . . . . . . . . . . . . . . . . 52

9 Results and Conclusions 54

9.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

9.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

10 Future Work 60

References 62

APPENDIX - A


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List of Figures

Pg.No

Figure 1.1 Block diagram illustrating the process flow. . . . . . . . . . . . . . . . . 2

Figure 1.2 The electromagnetic spectrum. . . . . . . . . . . . . . . . . . . . . . . . 3

Figure 1.3 The difference between hyperspectral images and other images. . . . . . 4

Figure 1.4 The original image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 1.5 Subset image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Figure 3.1 Components of a remote sensing system. . . . . . . . . . . . . . . . . . 9

Figure 3.2 A whiskbroom scanner.. . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Figure 3.3 A pushbroom scanner. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 3.4 The Hyperion sensor assembly . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 4.1 Spectral profile of water. . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 4.2 Spectral profile of vegetation. . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 4.3 Spectral profile of land.. . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 4.4 Spectral profile of built up areas. . . . . . . . . . . . . . . . . . . . . . . 16

Figure 5.1 Ground truth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Figure 5.2 Vegetation appears as red in the false colour composite image. . . . . . . 19

Figure 6.1 Radiation entering a sensor. . . . . . . . . . . . . . . . . . . . . . . . . 20

Figure 6.2 Atmospheric corrected images . . . . . . . . . . . . . . . . . . . . . . . 26

Figure 6.3 Image prior to atmospheric correction. . . . . . . . . . . . . . . . . . . . 27

Figure 6.4 Spectral profile of Ulsoor Lake before and after atmospheric correction. . 28

Figure 7.1 Illustration of the PCA.. . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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Figure 7.2 Pixel vector in PCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 7.3 Principal components one and two . . . . . . . . . . . . . . . . . . . . . 33

Figure 7.4 Principal components three and four.. . . . . . . . . . . . . . . . . . . . 33

Figure 7.5 Principal components five and six. . . . . . . . . . . . . . . . . . . . . . 34

Figure 8.1 Training dataset for ground truth image. . . . . . . . . . . . . . . . . . . 38

Figure 8.2 Illustration of the idea behind SAM. . . . . . . . . . . . . . . . . . . . . 40

Figure 8.3 Endmember collection spectra generated from ground truth image. . . . . 41

Figure 8.4 SAM classified output images. . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 8.5 Mahalanobis Distance classified output images. . . . . . . . . . . . . . . 44

Figure 8.6 Concept of Minimum Distance Classifier. . . . . . . . . . . . . . . . . . 46

Figure 8.7 Minimum Distance classified output images. . . . . . . . . . . . . . . . 47

Figure 8.8 Concept of Maximum Likelihood Classifier.. . . . . . . . . . . . . . . . 49

Figure 8.9 Maximum Likelihood output images. . . . . . . . . . . . . . . . . . . . 50

Figure 8.10k-means classification for FLAASH corrected image. . . . . . . . . . . . 53

Figure 9.1 Graph illustrating the variation of Mahalanobis classification accuracy

with number of principal components. . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 9.2 Graph illustrating the variation of Minimum distance classification accu-

racy with number of principal components. . . . . . . . . . . . . . . . . . . . . . 55

Figure 9.3 Graph illustrating the variation of SAM accuracy with number of princi-

pal components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figure 9.4 Graph illustrating the variation of Maximum Likelihood classification

accuracy with number of principal components. . . . . . . . . . . . . . . . . . . 56

Figure 9.5 Graph illustrating the variation of Mahalanobis classification accuracy

with number of principal components for QUAC corrected image. . . . . . . . . 57

Figure 9.6 Graph illustrating the variation of Minimum Distance classification ac-

curacy with number of principal components for QUAC corrected image. . . . . . 57

Figure 9.7 Graph illustrating the variation of Maximum Likelihood classifier accu-

racy with number of principal components for QUAC corrected image. . . . . . . 58

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Figure 9.8 Graph illustrating the variation of SAM classification accuracy with num-

ber of principal components for QUAC corrected image. . . . . . . . . . . . . . . 58

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List of Tables

Pg.No

Table 1.1 The three colours and their wavelengths. . . . . . . . . . . . . . . . . . . 3

Table 1.2 Differences between multispectral signals and hyperspectral signals.. . . . 4

Table 1.3 Table listing the details of the subset image.. . . . . . . . . . . . . . . . . 5

Table 5.1 Removed bands and the reason for their removal. . . . . . . . . . . . . . . 17

Table 5.2 The various bands used and their uses. . . . . . . . . . . . . . . . . . . . 18

Table 6.1 Details of various parameters required to perform FLAASH. . . . . . . . . 26

Table 8.1 SAM Classification efficiency for FLAASH corrected image. . . . . . . . 42

Table 8.2 SAM Classification efficiency for QUAC corrected image. . . . . . . . . . 42

Table 8.3 Mahalanobis Distance Classification efficiency for FLAASH corrected

image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Table 8.4 Mahalanobis Distance Classification efficiency for QUAC corrected image. 44

Table 8.5 Minimum Distance Classification efficiency for FLAASH corrected image. 47

Table 8.6 Minimum Distance Classification efficiency for QUAC corrected image. . 48

Table 8.7 Maximum Likelihood Classification efficiency for FLAASH corrected im-

age. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Table 8.8 Maximum Likelihood Classification efficiency for QUAC corrected image. 51

Table 8.9 k-means Classification efficiency for FLAASH corrected image . . . . . . 53

Table 9.1 Classification accuracy for FLAASH and QUAC correction modules. . . . 54

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2012-13 Hyperspectral Signal Processing Approach to Identify Land Cover Pattern

Abstract

Land cover assessment plays a vital role in several issues pertaining to policy making, directly

and indirectly affecting the lives of many people. Accurate information pertaining to land cover

is therefore of great importance.

The aim of this project is to to automate the process of classifying land cover into different

classes using images acquired by a hyperspectral sensor. The four classes of interest are: water,

vegetation, barren land, and built up areas.

This is a Level 1 classification, meaning that the main classes are not further subclassified

into various other classes.The hyperspectral images are passively sensed using optical signals.

The sensed signals are processed to remove the effects of atmosphere.

Atmospheric correction is performed using two techniques, namely, QUAC(Quick Atmo-

spheric Correction) and FLAASH(Fast Line of Sight Analysis of Spectral Hypercubes). Di-

mensionality reduction is done through Principle Components Analysis(PCA).

Automatic classification of the image into the target classes was done through the use of

supervised as well as unsupervised algorithms. Some of the supervised algorithms used were:

Mahalanobis Distance Classifier, Spectral Angle Mapper, etc. The unsupervised algorithm

used was thek-means algorithm.

The results indicate that supervised methods are the best classifiers and that among the

atmospheric correction techniques, the FLAASH correction module is the best on.

Department of Electrical and Electronics Engineering, AIeMS v


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2012-13 Hyperspectral Signal Processing to Identify Land Cover Pattern

Chapter 1

Introduction

Land cover assessment and analysis plays a very vital role in framing various national policies

of governments across the globe. Information obtained from land cover analysis gives informa-

tion pertaining to the environment, agricultural patterns, estimation of forest cover, preparation

of digital maps and are also used for military purposes.

Policies and government decisions regarding various issues pertaining to the aforemen-

tioned domains are based on the information available at hand. Therefore, accurate realisation

of land cover is of vital importance, as any wrong or inaccurate information results in bad poli-

cies and decisions jeopardising the security of the nation and endangering the livelihoods of

millions.

Land cover refers to what is actually present on the ground, describing the physical state of

the earths surface and immediate surface in terms of natural environment(water, vegetation,

rocks, etc) and man-made objects.

Land cover shouldnt be confused with the term land use, even though they are used inter-

changeably. Land cover provides information as to what features are present on the ground.

For example, land cover

Prior to automatic classification, land cover and land use assessment was done manually

by analysing aerial photographs of the area to be studied. As technology increased, automatic

Department of Electrical and Electronics Engineering, AIeMS 1


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classification techniques to classify the image, the need for automatic classification techniques

was felt.

There are several advantages of using automatic classification techniques. Previously, mul-

tispectral sensors were being used to acquire images. Multispectral sensors contain on an

average ten bands of the same area. However, this is not the case with hyperspectral im-

ages. Hyperspectral images contain hundreds of bands of the same area, each band separated

by a very small wavelength. Manually analysing hundreds of bands is an impossible task.

Moreover, humans are prone to errors and bias, which negatively influences the accuracy of

classification. Therefore, the only viable solution left is automatic classification.

1.1 Methodology

Prior to classification, various other procedures have to be performed, these are illustrated in

the form of a block diagram in Fig 1.1The steps written in logical order are as follows:

1. Data Preparation

2. Pre-Processing and Atmospheric Correction

3. Dimensionality Reduction

4. Classification

5. Validation using Ground Truth

Figure 1.1: Block diagram illustrating the process flow.



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The flow as illustrated in the block diagram, was arrived at by referring to the literature, and

other requirements which had to suit the specifications of the dataset we possessed.[ 1].

1.2 About hyperspectral signals

Normal images are acquired in a very small portion of the electromagnetic spectrum, this being

the visible region. The visible region comprises of wavelengths corresponding to three colours,

red, blue and green. The wavelengths corresponding to the three colours are given in Table 1.1

Colour Wavelength

Red 700 nm

Blue 400nm

Green 500 nm

Table 1.1: The three colours and their wavelengths.

The area occupied by the visible region is a very small portion of the electromagnetic spec-

trum, as can be seen from Fig 1.1

Figure 1.2: The electromagnetic spectrum.

Hyperspectral signals acquire information, in this case, images in the visible region as well

as the infrared region. The main advantage of using hyperspectral signals is that the difference

between each wavelength(also referred to as spectral resolution) is very small, approximately

10 nm. This fine spectral resolution results in a lot of information being acquired.A better way

to visualise the differences between hyperspectral images and other forms of remote sensing is

illustrated in Fig 1.3on the next page.



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Figure 1.3: The difference between hyperspectral images and other images.

Hyperspectral imaging is also referred to as imaging spectroscopy, due to the fact that differ-

ent materials can be reasonably identified using their spectrum. Mathematically, the spectrum

of a continuous signal,x(t)can be written as [2]:

X() = +

x(t)etdt (1.1)

Eq 1.1 is also known as the Fourier transform of the signal x(t).

Multispectral Hyperspectral

Discrete coverage of the spectrum Continuous coverage of the

spectrum

Spectral resolution is around 100 nm. Spectral resolution around

10-40 nm

Low information content compared to hyperspectral signals Greater information content

Table 1.2: Differences between multispectral signals and hyperspectral signals.

1.3 Geographical area under study

The area of study is an image of Bangalore acquired in the year 2002, by the Hyperion sensor

onboard the EO-1 satellite. The image ranges from Nandi Hills in the north to the outskirts of

Kanakapura in the south. The image has spatial dimensions 911x3191, and a spectral dimen-

sion of 196 bands in the image.

A subset of the image was chosen, primarily due to the lack of ground truth for the outlying

areas of the image, and any subsequent ground truth prepared would not have reflected the out-

lying areas as of 2002. The subset of the image varies from the outskirts of Yelahanka, in North

Bangalore to the areas adjoining Banneraghata National Park in South Bangalore. Referring



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to Google Earth, we observed no significant changes in these areas, and hence these particular

areas were chosen to form the subset image.

Details of the subset image are given in the form of a table (Table 1.3) on the next page,

along with the original image (Fig 1.4) and the subset image(Fig 1.5). The table lists various

parameters, such as latitude, longitude, the spatial and spectral dimensions. The black portion

in Fig 1.4 is indicative of areas which do not fall under the sensors view.

Parameter Details

Latitude of UL corner 13.07721667 N

Longitude of UL corner 77.57472500 E

Latitude of LR corner 12.91586889 N

Longitude of LR corner 77.61036142 E

Rows 750

Columns 500

Spatial resolution 30mx30m

Table 1.3: Table listing the details of the subset image.

Figure 1.4: The original image.



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Figure 1.5: Subset image

1.4 Outline of the Report

The rest of the report is arranged as follows:

Chapter 2 describes the previous attempts at land cover classification, along with a section

on other applications of hyperspectral signals.

Chapter 3 describes in detail the methods of image acquisition.

Spectral profiles are the pillars of imaging spectroscopy, and we talk about this in Chapter

4.

Chapter 5 describes the way the acquired data is prepared for pre-processing.

In Chapter 6 the techniques of atmospheric correction, the need for atmospheric correc-

tion, and the effects of atmospheric correction on the image are discussed.

Chapter 7 talks about dimensionality reduction in general, and Principle Components

Analysis in particular.

Chapter 8 deals with the various algorithms used for classification, the mathematical back-

ground of the algorithms used to classify the image.

The results obtained for various classification algorithms are summarised and discussed

in Chapter 9.

Chapter 10 indicates the road ahead for hyperspectral imaging research.



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Chapter 2

Literature Survey

In this chapter, we take a look at previous attempts at land cover classification using satellite

images and later on in the chapter, look at the many uses of hyperspectral images and gauge

the broad spectrum of areas across which hyperspectral signals are being used.

Remotely sensed images, be it multispectral images or hyperspectral images are nowadays

being used for many end uses. Of these many uses, land cover classification is one.

Uttam Kumar,et.al., have given a clear picture of the detailed history behind land cover clas-

sification using satellite images in [3].

Land cover classification using satellite images has a long history. The first use of satellite

images for land use classification was started in the late 1970s and early 1980s as part of the

National Mapping Program, by the United States Geological Survey, based on images acquired

by NASA. The images were acquired using aerial photography and classification was carried

out manually by analysts[3].

Later on, in 1991, the entire country of China has been mapped to produce a digital map

containing 20 different land use /land cover classes. This was done using field surveys, satellite

and aerial images to understand land cover change.[4]

More attempts at land use/land cover classification include the National Land Cover Database



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provides a standardised land cover database for South Africa, Swaziland and Lesotho using

Landsat images acquired in 1994-95, the Global Vegetation Monitoring unit of the JRC,ISPRA

in Italy to generate a global land cover/land use mapper in the year 2000[3].

In India, land use and land cover study using remote sensing has been initiated by the Indian

Space Research Organisations remote sensing satellite; Resourcesat and the National Remote

Sensing Agency, Department of Space. The major classes of interest were agricultural areas,

surface water bodies, waste lands, forests,etc. This was carried out on a national level using

multi-temporal IRS(Indian Remote Sensing), AWiFS(Advanced Wide Field Sensor) datasets

to provide on an annual basis, net sown areas for different cropping seasons and an integrated

land cover map[4].Senthilnath et.al, have used multi-spectral satellite images[5] to determine

land cover over the city of Bangalore in the year 2010. Uttam Kumar,et.al,[3] have used hy-

perspectral data acquired by MODIS satellite to assess land use pattern over the district of

Chikkaballapur in the state of Karnataka, India.

Other uses of hyperspectral signals include assessing the impact of climate change[6],detecting

the type of crops and their growth stage[7],[8], medical imaging[9],face recognition[10]. The

applications are not limited to the aforementioned areas, it can be used in many other areas,

limited only by our imagination.

To the best of our knowledge, assessing land cover using hyperspectral data acquired by the

Hyperion sensor hasnt been done till now.



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Chapter 3

Image Acquisition

The sensor used to acquire the image is the Hyperion sensor onboard the Earth Observing-

1(EO-1) satellite, launched by NASA. The Hyperion sensor is a hyperspectral sensor.

3.1 Sensor Models

The sensor used in any satellite based or airborne imager maybe modelled as depicted in the

figure below. It has been adapted from[11].

Figure 3.1: Components of a remote sensing system.

3.1.1 Scanner

There are basically two types of scanning methods: a)Along track scanning and b)Across track

scanning.



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Across track scanning

InAcross track scanninguses rotating and oscillating mirror which scans the entire terrain that

are at right angles to flight line. Successive scan lines are covered as aircraft moves forward,

yielding a series of contiguous strips of a 2-D image. These type of sensors are also called

whiskbroom scanners(Fig 3.2).

Figure 3.2: A whiskbroom scanner.

At any instant, scanner detects energy within Instantaneous Field Of View (IFOV), which

is normally expressed as cone angle within which incident energy is focussed on detector.is

determined by instruments optical system and size of detectors.

D= h (3.1)

where, D = Diameter of circular ground viewed

h = Flying height above terrain

= IFOV in radians

Along track scanning

InAlong track scanning, the sensor unit consists of a linear array of detectors, which consists

of Charge Coupled Devices(CCDs). The direction of scanning is the same as the direction of

motion of the imaging platform, hence the name,along track scanning. These type of sensors



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are also called pushbroom scanners(Fig 3.3).

Figure 3.3: A pushbroom scanner.

The main advantages of using these kinds of sensors is that since they do not contain any

moving parts, their service life is high, and they have a longer dwell time, i.e., the time spent

on each scan line. A longer dwell time implies that the signal strength recorded is high. Due

to these advantages over across track scanning, the Hyperion sensor uses along track scanning

method[12].

3.1.2 Imaging Optics

Optics usually describes the behavior of visible, ultraviolet, and infrared light used in imaging.

Here optical system is used to project information collected in sensors onto detectors. Before

optical signals are projected onto detectors they are made to split into there constituent regions

of the electromagnetic spectrum with help of prisms and dichroic grating. The dichroic grating

splits optical signal into thermal and non thermal regions. The prism is then used to split the

non thermal optical signals into visible, ultra-violet(UV) and near Infrared regions.

3.1.3 Detectors

Due to sensor platform and scan mirror velocity, various sample timings for bands and pixels,

the need to physically separate different spectral bands, and limited availability on focal plane,



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the detector patterns are often in different pattern as per requirement. Here, each detector in-

tegrates the energy that strikes surface(irradiance) to form measurement at each pixel. Then

integrated irradiance at each pixel is converted into electrical signal and quantized as integer

value, called as Digital Number(DN). A finite no. of bits, Q, is used to code the continuous

data measurements as binary numbers. The no. of Discrete Numbers is given by,

NDN= 2Q (3.2)

and DN can be any integer in the the range,

DNRANGE= [0, 2Q 1] (3.3)

The larger the value of Q, the more closely the quantized data approximates the original con-

tinuous signal generated by detectors, and the higher will be the radiometric resolution of that

sensor[11].

3.2 Mathematical Model of the Instrument

No instrument can measure the signal it has sensed with 100% accuracy, because the signal is

always varying as a function of some parameter. This parameter, in the case of remote sensing,

is usually time, or wavelength, space. So, to obtain the output signal, the instrument must

integrate the signal over a non-zero parameter value. This can be written as[11]:

o(z0) =

W

i()r(z0) (3.4)

wherei is the input signal r(z0 ) is the instrument response, inverted and shifted by

z0o(z0)is the output of the instrument at z=z0W being the range over which the integral is

significant, W also depends on the parameter of integration.

What the above equation means is that the output signal of the instrument is the convolu-

tion of the input signal,(the signal being sensed) and the instrument response. Written more

concisely, Eq 3.1 can be rewritten as

o(z0) =i(z)r(z) (3.5)



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3.3 Hyperion Sensor

As discussed above, the Hyperion sensor is a pushbroom sensor. Each pushbroom image frame

captures an area of approximately 30m along track, by 7.7km cross track[ 12]. The Hyperion

optics is a three mirror astigmate design with a 12-cm primary aperture and a f-number of

11. The sensor acquires the signals reflected of the image at an altitude of 705-km above the

surface of the earth. Hyperion also has two onboard spectrometers, a VNIR spectrometer and

a SWIR spectrometer. The spectrometers are temperature controlled at 2932K, and 283K for

the SWIR and VNIR spectrometers respectively.

Figure 3.4: The Hyperion sensor assembly



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Chapter 4

Spectral Profiles

Spectral profiles are a graph of reflectance versus wavelength. It indicates at wavelengths, a

material has maximum reflection, or minimum reflection.

4.1 Water

The variation between the spectral profiles of the four classes is explained as follows. Water

acts as an absorber in the IR(Infra-Red) region. The infra red region varies from 700 nm to

1 mm. Maximum water absorption occurs at 1450 nm, which is the SWIR region of the IR

region, as can be seen from the Fig 4.1.

Figure 4.1: Spectral profile of water.

4.2 Vegetation

The spectral profile of vegetation is a function of the chlorophyll content present. In this case,

a minor peak occurs at the wavelength corresponding to the colour green. This indicates that



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the vegetation is photosynthetically active. The troughs at the red and blue wavelengths occur

because the wavelengths in these regions are absorbed to satisfy the energy requirement for

photosynthesis. The reflectance remains very high in the NIR region, because of interaction

between the leaf tissues and electromagnetic radiation.

A dip can be seen in the SWIR region, because water absorption, due to water present in the

leaves and stem, predominates at these wavelengths.

Figure 4.2: Spectral profile of vegetation.

4.3 Land

The reflectivity of barren land depends on the soil type, moisture content, soil texture, and or-

ganic matter present in the soil. From Fig 4.3, it can be seen that there is maximum reflectivity

in the NIR region, and a very low reflectivity in the SWIR region. This indicates that the land

under study has a very high moisture content.

Figure 4.3: Spectral profile of land.



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4.4 Urban areas

Urban areas and land have similar spectral properties. The reflectivity of built up areas, is again

dependent on various factors such as the type of material used,the moisture content present in

the materials, etc. Here, the spectral profile of urban areas indicates a high moisture content in

the materials.

Figure 4.4: Spectral profile of built up areas.



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Chapter 5

Data Preparation

For Level 1 radiometric image, out of the 224 bands used to acquire the image, only 198 bands

have been calibrated. Calibrated channels are 8-57 for VNIR, and 77-224 for SWIR. All the

channels are not calibrated because of detectors low response and hence bands that are not

calibrated are set to zero. The remaining 26 bands do not contain any useful information. The

bands which are removed and the reasons for their removal are listed in the form of a table

below.

Removed bands Reason for removal

Bands 1-7 No useful information

Bands 58-76 No useful information

Bands 77-78 Spectral overlap

Bands 56-57 Spectral overlapBands 225-242 No useful information

Table 5.1: Removed bands and the reason for their removal.

The digital values of Level 1 images are 16 bit radiances and are stored as 16 bit signed

integer. After all the unwanted bands are removed, the remaining 196 bands are stacked on

top of another. The stacked image is stored in the Band Interleaved Format(BIL), using ENVI

4.7. The wavelengths and the Full Wavelength at Half Maximum(FWHM) for each band are

specified using data specified in the Hyperion Data Format Control Book[13].

The human eye can detect only three colours and their various combinations, hence the im-

age is visualised in three bands.



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Band Numbers Use

Band 29(Red),Band 23(Green),Band 16(Blue) True Colour Composite

Band 50,Band 23,Band 16 False Colour Composite(Vegetation

appears as Red)

Table 5.2: The various bands used and their uses.

A subset of the image was taken. The primary reason for this was due to the fact that there

was no ground truth available for the entire image. Any subsequent ground truth prepared

wouldnt reflect any changes in the outskirts of the city. Another reasons for using a subset of

the image was due to the fact that analysis of the image was computationally not feasible.

5.1 Ground Truth

Ground truth refers to what is actually present on the ground. The ground truth is used as a

reference to validate the classifying accuracy of different algorithms used.

The ground truth was prepared by dividing the subset image into 40 clusters using the k-

means technique. Using Google Earth images of Bangalore acquired in 2002, and False Colour

Composites of the subset image, the 40 classes were merged and finally four different classes

corresponding to water, vegetation, built up area and land.

Figure 5.1: Ground truth.



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The false colour composite was used to find the areas containing vegetation, since vegetation

appears as red in the false colour composite image.The false colour composite image in which

vegetation appears as red is shown below.

Figure 5.2: Vegetation appears as red in the false colour composite image.



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Chapter 6

Atmospheric correction

The process, which transforms the data from spectral radiance to spectral reflectance, is known

as Atmospheric Correction, Compensation or Removal[14]. Hyperion images are rich source

of information contained in hundreds of narrow contiguous spectral bands. There are number

of atmospheric agents which contaminate this information contained in various bands. There-

fore to get complete advantage of Hyperion data its required to remove the effects of such

atmospherical agents on earth observation data.

6.1 Need for Atmospheric Correction

Earths atmosphere is not clear or plain, but it contains a lot of dust particles, aerosols, water

vapour molecules, carbon particles, etc due to which reflectance path and amount of spectra

between source i.e sun and pixel under observation and between pixel under observation and

sensors is altered. Hence actual parameters are not acquired and hence we have to correct these

defects in order to get original information. This is illustrated in Fig 6.1.

Figure 6.1: Radiation entering a sensor.



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where,S1 is radiance to be observed,

S2 is radiance from atmospheric dispersion,

S3 is Path Radiance.

It is a critical pre-processing step since most approaches have been implemented using spec-

tral library or field spectra[14]. If atmospheric correction is not performed, then there is marked

difference between observed spectral irradiance and spectral library or field spectra. These dif-

ferences will negatively influence accuracy to which classification has been carried out based

on field spectra or independent spectral library.

The atmosphere effects the brightness or radiance, recorded over any point on the ground in

two almost contradictory ways, when sensor records reflected solar energy. First it attenuates

energy illuminating a ground object at particular wavelengths, thus decreasing radiance that can

be measured. Second, the atmosphere acts as a reflector itself, adding a scattered, extraneous

path radiance to signal detected by sensor which is unrelated to properties os surface. By

expressing these two atmospheric effects mathematically, the total radiance recorded by the

sensor may be related to the reflectance of the ground object and the incoming radiation or

irradiance using following equation:

Ltot=ET

+L (6.1)

where,

Ltot= total spectral radiance measured by sensor

= reflectance of object

E = irradiance on object, incoming energy

T = transmission of atmosphere

L= path radiance, from atmosphere and from the object

All these factors depend on wavelength. The irradiance (E) stems from two sources: di-

rectly reflected sunlight and diffuse skylight (sunlight scattered by the atmosphere). The rela-

tive dominance of sunlight versus skylight in a given image is strongly dependent on weather



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conditions. The irradiance varies with the seasonal changes in solar elevation angle and the

changing distance between the earth and the sun[3].

The magnitude of absorption and scattering varies from place to place and time to time

depending on the concentrations and particle sizes of the various atmospheric constituents.

The end result is the raw radiance values observed by a hyperspectral sensor that cannot be

directly compared to laboratory spectra or remotely sensed hyperspectral imagery acquired at

other times or places. Before such comparisons can be performed, an atmospheric correction

process must be used to compensate for the transient effects of atmospheric absorption and

scattering.

6.1.1 Reflectance v/s Radiance

In all the atmospheric correction techniques which are going to be described in this report, ra-

diance is converted to reflectance. Information, when first acquired, is in the form of radiance.

Radiance, also known as spectral radiance, is a measure of the quantity of radiation, that

passes through, or is emitted from the surface and falls within a given solid angle in a specified

direction. Radiance has units ofW.sr1m3. The radiation of an object can be affected by the

radiance of other objects in its surroundings.Energy transfer occurs between different objects,and thus the signal acquired does not truly reflect the object under observation. Fig?? gives a

pictorial representation of this explanation.

Reflectance,is a measure of the ability of a surface to reflect light or other electromagnetic

radiation, equal to the ratio of the reflected flux to the incident flux. Reflectance values, give

a true picture of the object under study, because reflectance value of an object is unique and

thus, this reflectance value acquired across the entire spectral range of the sensor, gives us the

spectral signature of the object under study.

6.2 Atmospheric Correction Approaches

Atmospheric correction may be applied by collecting information from scene i.e Scene Based

Empirical Approaches or by modelling radiation transmission through atmosphere i.e Radia-



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tion Transport Model based Approach.

6.2.1 Scene Based Empirical Approach

These approaches are based on radiance values present in image i.e scene, hence name Scene

Based Empirical Approaches. IAR (Internal Average Reflectance) and ELM (Empirical Line

Method) and QUAC (Quick Atmospheric Correction) are some of major examples of Scene

Based Empirical Approaches.

6.2.2 Radiation Transport Model Approach

The Scene Based Empirical Approach are not generally producing good results as the linear-

ity assumption, which presumes uniform atmospheric transmission, scattering and adjacency

effects throughout atmosphere, which may not be accurate. But Radiation Transport Model

tries to understand and remove effects of major atmospheric process with radiation such as

absorption and scattering. Very effective and latest models are MODTRAN (MODerate reso-

lution atmospheric TRANsmission) and FLAASH (Fast Line of Sight Atmospheric Analysis

of Spectral Hypercubes.

QUAC and FLAASH atmospheric correction modules were performed on the stacked im-

age. These two methods are chosen because they are effective for atmospheric correction

of multispectral and hyperspectral data. The two correction modules are implemented using

ENVI 4.7.

6.3 QUAC

QUAC i.e Quick Atmospheric Correction is a scene based empirical approach. It determines

the atmospheric compensation parameters directly from the information contained within the

scene using the observed pixel spectra, requiring only approximate specification of sensor band

locations (i.e., central wavelengths) and their radiometric calibration and no additional informa-



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tion is required[15]. The approach is based on the empirical finding that the spectral standard

deviation of a collection of diverse material spectra, such as the constituent material spectra in

a scene, is essentially spectrally flat.

It allows the retrieval of reasonably accurate reflectance spectra even when the sensor does

not have a proper radiometric or wavelength calibration, or when the solar illumination inten-

sity is unknown. The computational speed of the atmospheric correction method is signifi-

cantly faster than for the first-principles methods, it is significantly faster than physics based

approaches, making it potentially suitable for real-time applications. The aerosol optical depth

retrieval method, unlike most prior methods, does not require the presence of dark pixels.

QUAC creates an image of retrieved surface reflectance, scaled into two byte signed integers

using reflectance scale factors of 10,000.

6.4 FLAASH

Fast Line of Sight Atmospheric Analysis of Spectral Hypercubes, dubbed as FLAASH is an

atmospheric correction technique developed by the Air Force Research Laboratory, Space Ve-

hicles Directorate (AFRL/VS), U.S. Air Force, to support the analysis of VNIR to SWIR (from

0.4m to 3m ) hyperspectral and multispectral imaging sensors.

The main objectives are to provide accurate, physics-based derivation of atmospheric prop-

erties such as surface pressure, water vapor column, aerosol and cloud overburdens, to incor-

porate those same quantities into a correction matrix, and, finally, to invert radiance-at-detector

measurements into reflectance-at-surface values. Atmospheric correction serves a critical role

in the processing of remotely sensed image data, particularly with respect to identification of

pixel content.

Efficient and accurate realization of images in units of reflectance, rather than radiance, is

essential for building consistency into the development, maintenance, distribution, and analy-

sis of any library of such images, acquired under a variety of measurement conditions. Unlike



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other atmosphere correction algorithms that interpolate radiation transfer properties from a pre-

calculated database of modelling results, FLAASH incorporates the MODTRAN-4 radiation

transfer code. User is provided a option of choosing any one of the standard MODTRAN model

of atmospheres and aerosols types to represent the scene and a unique MODTRAN solution is

computed for each image.

FLAASH processes radiance images with spectral coverage from the mid-IR through UV

wavelengths where thermal emission can be neglected. For this situation the spectral radiance

L* at sensor pixel may be parameterized as

L

=

A

1eS+

Be1eS+L

a (6.2)

where,

= pixel surface reflectance

e= average surface reflectance for surrounding region

S = Spherical albedo of the atmosphere (capturing backscattered surface reflected photons)

La= Radiance backscattered by atmosphere without reaching the surface

A,B = Surface independent co-efficients that vary with atmosphere and geometric conditions

Various options corresponding to different parameters such as the sensor type,the altitude

of the sensor acquired from the ground, pixel size, the type of environment the image was

acquired in, can be incorporated in FLAASH. All these parameters and others essential to

carry out FLAASH for the image under study is listed in the form of a table (Table 6.1) on the

next page.

Spectral polishing was carried out since it it reduces the noise of the obtained spectra. This

was done using the average of the neighbouring multiple bands. For Hyperion data, a value of

9 was chosen for spectral polishing[16].



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Parameter Details

Sensor Type Hyperion

Sensor Altitude 705.00 km

Pixel Size 30.00 m

Latitude of scene center 12.41810036 N

Longitude of scene center 77.57769775

EFlight date 22nd March,2002Atmospheric Model Tropical

Aerosol Model Urban

Zenith Angle 148.655303

Azimuth Angle 110.924797

Table 6.1: Details of various parameters required to perform FLAASH.

6.5 Comparison of the various atmospheric techniques

A comparison between the atmospheric techniques used to correct the images in our project is

done in this section. Fig6.2 illustrates the subset image after performing atmospheric correc-

tion, and Fig6.3prior to atmospheric correction.

(a) Image after QUAC Correction (b) Image after FLAASH Correction

Figure 6.2: Atmospheric corrected images

It is evident from the three images, that interpreting the effects of atmospheric correction by

visual inspection of the images is not a easy task. Using the spectral profile one can make out



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Figure 6.3: Image prior to atmospheric correction.

changes in the images before and after atmospheric correction, refer to Fig6.4.

6.5.1 Explanation of the water spectral profile

Prior to atmospheric correction, the reflectance of water is very high in the VNIR and SWIR re-

gions. As mentioned in the previous chapter, this is not the case. After atmospheric correction,

the reflectance of water has reduced in these bands. This might seem counter-intuitive, but this

unexpected result can be accounted for. Pure water,viz, water that is without any impurities, or

almost negligible amount of impurities has a low reflectance in the IR region. But, the water

body in our area of study, is not pure. It contains a large amount of organic matter, making it

impure, contributing to higher values of reflectance in the IR region. After atmospheric cor-

rection, due to the impurities being removed, the reflectance reduces to a minimum in the IR

region, as expected, for the spectral profile of water.



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Figure 6.4: Spectral profile of Ulsoor Lake before and after atmospheric correction.

Another thing to be noted is that the spectral profile after QUAC is not a continuous curve.

It is not defined for certain wavelengths. This is rather expected, since QUAC is a scene inde-

pendent, empirical method of atmospheric correction, and hence these anomalies are observed.



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Chapter 7

Dimensionality Reduction

Hyperspectral images, as described earlier, are made up of hundreds of narrow contiguous

bands, with a very fine spectral resolution. Due to the very fine spectral resolution, two or

more bands may contain very much the same data(high correlation) and thus, this informa-

tion is redundant.Redundant information occupies extra storage space, it also means that when

analysing or classifying, the same information has to be looked at again,reducing the compu-

tational speed.This poses significant challenges in terms of their analysis. Due to their high

dimensional space,they suffer from the curse of dimensionality and another challenge to be

factored in, is the Hughes phenomenon[17]. In high dimensional datasets, an enormous num-

ber of training datasets are required to ensure that multiple samples are covered. This is known

as the Hughes phenomenon.

To overcome these challenges, the dimension of the hyperspectral images are reduced to an

acceptable number. Acceptable, here is defined by the classifier accuracy.

There are many methods of dimensionality reduction. Examples include Principle Com-

ponents Analysis(PCA), Independent Components Analysis (ICA), Vertex Component Analy-

sis(VCA),etc.

ICA is based on the fact that a signal(in any number of dimensions) is composed of statis-

tically independent signals. But, a major drawback of ICA is that it doesnt work on gaussian

datasets.Since our dataset is very big, we work under the assumption that our dataset obeys the



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Central Limit Theorem, and hence is gaussian in its behaviour.

VCA assumes that the spectrally pure endmembers constituting the hyperspectral image lie

at the vertices of a n-d polygon in n-dimensional space.

PCA is chosen, because in terms of computation speed, it is the fastest[18].

7.1 Principle Components Analysis

The method of principal components, also known as the Karhunen-Loeve Transform, or the

Hotelling Transform is a data-analytic technique that obtains linear transformations of a group

of correlated variables such that certain optimal conditions are satisfied. The most important

of these conditions is that the transformed variables are uncorrelated[19].

The number of principle components is less than or equal to the number of original com-

ponents. If a multivariate dataset, visualised as a set of coordinates in a high-dimensional data

space, PCA provides a lower-dimensional picture. This is done using only the first few princi-

pal components.

Figure 7.1: Illustration of the PCA.



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7.1.1 Mathematical Description

Mathematically, PCA is defined as an orthogonal linear transformation, that transforms the

data to a new coordinate system, refer Fig7.1.This orthogonal linear transformation, is done in

such a manner such that the greatest variance by a projection of the data lies along the first co-

ordinate(the first principal component), the second greatest variance on the second coordinate

and so on.

For hyperspectral images, the PCA is illustrated below. The figure has been adapted from[20]

Figure 7.2: Pixel vector in PCA.

The PCA is based on the mathematical principle of eigenvalue decomposition of the covari-

ance matrix of the hyperspectral image to be analysed. First, an image pixel vector is calculated

as, follows (refer Fig 7.2).The following description has been adapted from[20]:

xi = [x1, x2, . . . , xn]iT,fori= 1, 2, . . . , M (7.1)

wherex1, x2, . . . , xn , represent the pixel value of the hyperspectral image at a pixel location

andTdenotes the transpose,

andMrepresents the dimensions of the hyperspectral image(mxn),

m being the number of rows, and n being the number of columns,

N represents the number of bands in the hyperspectral image.

The mean vectorm of all the image vectors [x1, x2, . . . , xn]Ti is calculated for i= 1, 2, . . . , M .

The mean vectormis calculated as follows:



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m = 1

M[x1, x2, . . . , xn]

Ti (7.2)

The covariance matrix ofx is defined as:

Cov(x)= E{(xE(x))}(xE(x))T (7.3)

Eq 7.3can be approximated as follows:

CX= 1

M

Mi=1

(xim)(xim)T (7.4)

whereE is the expectation operator

The covariance matrix CXis eigen decomposed to give the following matrices:

CX= ADAT (7.5)

whereA is an orthonormal matrix containing the eigenvectors of the covariance matrix CX,

and D is a diagonal matrix containing the eigenvalues of the covariance matrix CX,

i.e.,D = diag(1, 2, . . . , N)

An orthonormal matrix is a matrix whose transpose is its inverse, i.e, in the case ofA,

AT = A1. (7.6)

A = (a1,a2, . . . ,aN)

The linear transformation which gives the principal components is described as:

yi = ATxi (7.7)

yi=

y1

y2...

yK...

yN

=

a11 a12 . . . a1K . . . a1N

a21 a22 . . . a2K . . . a2N...

... ...

... ...

...

aK1 aK2 . . . aKK . . . aKN

x1

x2...

xK...

xN

i

(7.8)



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If only the firstKeigenvectors are chosen, the first Kprincipal components of the image

are obtained.UsuallyK N, and hence this leads to reduction in the dimensionality of the

image.

Fig7.3, Fig7.4and Fig7.5shows the first six principal components of the study image.

As can be seen from Fig 7.3, Fig7.4 and Fig7.5, the first three principal components con-

tain the majority of the information. The information content reduces to a bare minimum in the

remaining two bands. It is evident that, theSNR(Signal to Noise Ratio) progressively reduces,

as we look at principal components away from the first three components.

(a) First principal component (b) Second principal component

Figure 7.3: Principal components one and two

(a) Third principal component (b) Fourth Principal component

Figure 7.4: Principal components three and four.



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(a) Fifth principal component (b) Sixth principal component

Figure 7.5: Principal components five and six.



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Chapter 8

Classification Algorithms

Classification includes a broad range of decision-theoretic approaches to the identification of

images. All classification algorithms are based on the fact that the image under consideration

depicts one or more features (e.g. spectral regions in the case of remote sensing) and that each

of these features belongs to one of several distinct and exclusive classes. These features are an-

alyzed as set of quantifiable properties and these properties may be variously be categorical(e.g

A, B, AB, or O as in case of blood group), ordinal (e.g. large, small ), integer

valued (e.g. like occurrences of word in a mail) or real-valued.

8.1 Methodology of classification

Classification can be primarily of two types, supervised classification and unsupervised clas-

sification. Supervised classification is a learning method wherein a training set of correctly

identified observations are available. An unsupervised procedure involves grouping data into

categories based on some measure of inherent similarity and this procedure is also called clus-

tering.

There are three steps involved in any classification process[21]. They are training, classifi-

cation and accuracy assessment. Training sites are needed for supervised classification and in

this study the training areas were taken from ground truth image. The satellite image is then

classified using four supervised classifiers and an one unsupervised classifiers. Two measure-

ments of accuracy assessment were carried out for each of the classifier methods mentioned

earlier, such as overall accuracy and error matrix or confusion matrix. Accuracy assessment



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was carried out to compute the probability of error for the classified map. The error matrix

is the term used to describe the measure of accuracy between the images that have been clas-

sified and the training side of the same image. The term accuracy here refers to degree of

correctness of the classification.

8.1.1 Training

The description of training classes is an extremely important component of the classification

process. In supervised classification, statistical processes (i.e. based on an a priori knowl-

edge of probability distribution functions) or distribution-free processes can be used to extract

class descriptors. Unsupervised classification relies on clustering algorithms to automatically

segment the training data into prototype classes. In either case, the motivating criteria for

constructing training classes is that they are:

independent, i.e. a change in the description of one training class should not change the

value of another,

discriminatory, i.e. different image features should have significantly different descriptions,

reliable, all image features within a training group should share the common definitive de-

scriptions of that group.

A convenient way of building a parametric description of this sort is via a feature vec-

tor(v1, v2, ...vn), where n is the number of attributes which describe each image feature and

training class. This allows us to consider each image feature as occupying a point, and each

training class as occupying a sub-space (i.e. a representative point surrounded by some spread,

or deviation), within the n-dimensional classification space. Viewed as such, the classification

problem is that of determining to which sub-space class each feature vector belongs.

8.1.2 Confusion matrix

A confusion matrix is a specific table layout that allows visualization of the performance of

an algorithm, typically a supervised learning one (in unsupervised learning it is usually called

a matching matrix). Each column of the matrix represents the instances in a predicted class,

while each row represents the instances in an actual class. The name stems from the fact that it



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makes it easy to see if the system is confusing two classes (i.e. commonly mislabeling one as

another). Confusion Matrix is also called as Error Matrix.

There are many unique image classification methods available to extract rich source of infor-

mation present in hyperspectral imagery. Most methods usually compare study image spectra

with reference image spectra (Ground truth image). The reference spectra can be obtained by

defining Regions of Interest, building spectral libraries, making measurements on field or by

directly extracting it from image pixels.

In this project,the following supervised classification algorithms have been applied;Spectral

Angle Mapper Classifier, Mahalanobis Distance Classifier, Minimum Distance Classifier and

Maximum Likelihood Classifier to classify the image into four different classes. The four

classes are Water, Vegetation, Built Up Areas and Land. Before applying supervised methods,

we need to train the dataset, about which, the explanation is given in the following section.

8.2 Training Data Set

In order to apply supervised classification algorithms on the images, a training dataset has to be

created from the ground truth data. This will be used to train the classifier and after training,the

remaining pixels of the image will be classified Half of the data from the ground truth was used

to create the training datasets for supervised classification algorithms. The following steps have

been followed to prepare the training datasets :

A copy of ground truth is saved as an excel file as it is easier to group similar values in excel.

Each pixel in ground truth is divided into 4 different classes and an additional class for

unclassified data and each pixel will have 5 different values being 1- Water 2 - Vegetation

3 - Barren Land 4 - Urban/Built up Area and 0 - Unclassified.

Four new excel sheets containing co-ordinates of each pixel in the class are created for the

four different classes.

The first excel sheet for Water class is taken and an extra column is added using random

number generator (Rand()) function in excel.



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The entire sheet is sorted from the lowest to highest according to values in the new column.

Half the number of pixels in the class, selected randomly, is stored in a different ASCII file.

This procedure is repeated for all the remaining classes four classes and four files each con-

taining randomly selected half of the ground truth pixels is created.

Using ENVI 4.7, these pixels are read into four different regions of interests (ROIs) and

thereby half of the data in ground truth is randomly selected as training datasets.

Figure 8.1: Training dataset for ground truth image.

Fig8.1 refers to training dataset with each class containing half the number of pixels in

ground truth image.

8.3 Spectral Angle Mapper

The Spectral Angle Mapper(SAM) is a classification method that permits rapid mapping by

calculating spectral similarity between image spectrums to reference reflectance spectras[22].

The reference reflectance spectra can be either taken from laboratory or field measurements or



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extracted directly from image. This technique was developed by Roberta, et al[22].

SAM is a very powerful classification method because its relatively unaffected by surround-

ing illumination conditions and targets highlights the target reflectance characteristics. But the

drawback of this method is the spectral mixture problem. The most erroneous assumption

made with SAM, is the assumption that the endmembers chosen to classify the image repre-

sents pure spectra of reference material whereas as in actual practice there is mixture of pixels

due to various physical phenomena.

SAM compares image spectra to known spectra which is present in ground truth image. It

takes arc cosine of the dot product between test spectrums t to reference spectrums r with

the following equation

= cos1

nb

i=1tiri

(

nb

i=1t2i)(

nb

i=1r2i

)(8.1)

where, nb = number of bands

ti= test spectrum

ri= reference spectrum

Smaller the spectral angle calculated, the correlation between the image spectrum and the

reference spectrum increases. Pixels which are further away than the specified maximum angle

threshold are not classified. The reference spectra for the Spectral Angle Mapper in our case

were generated by the average spectral profiles for each of the training class dataset. They can

also come from ASCII files, spectral libraries, statistics files.

In an n-dimensional multispectral or hyperspectral space, a pixel vectorx has both magni-

tude (i.e. length) and an angle measured with respect to axes that defines co-ordinate system

of the space[23]. In SAM, only the angular information is used. It is based on the idea that

an observed reflectance spectrum can be considered as a vector in n-dimensional space, where

number of dimensions is equal to the number of spectral bands. If the overall illumination



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increases or decreases, due to scattering of sunlight or shadows, the length of this vector will

increase or decrease respectively, but the angular orientation between the two vectors will re-

main constant.

Figure 8.2: Illustration of the idea behind SAM.

The concept of SAM can be explained with help of Fig8.2. Fig 8.2ashows that for a given

feature type, the vector corresponding to its spectrum will lie along the line passing passing

through the origin, with the magnitude of the vector being smaller(A) or greater (B) under

lower or larger illumination, respectively. Fig 8.2bshows comparison of the vector for an un-known feature type (C) to a known material with reference measured spectral vector(D), and

two features match if the angleis smaller than the specified tolerance value[24].

One major drawback of SAM is that it fails if the vector magnitude is important in provid-

ing discriminating information, which may happen in certain instances. However, if the pixel

spectra from different classes are well distributed in feature space there is high likelihood that

angular information alone will provide good separation.

Fig 8.3 shows reference spectra generated from the training dataset Regions of Interest(ROIs)

as described earlier in the chapter.



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Figure 8.3: Endmember collection spectra generated from ground truth image.

(a) SAM classification for QUAC Correction. (b) SAM classification for FLAASH Correction.

Figure 8.4: SAM classified output images.



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Confusion matrices were then generated for all SAM classified images to measure its effi-

ciency. Ground truth image was taken as reference for generating confusion matrix with respect

to classified output images for 196 band image and 5, 10, 15 band PCA images. These results

were then tabulated into table form as shown

Class name Sample Size Subset-196 PCA-15 PCA-10 PCA-5

(Pixels) bands(%) bands(%) bands(%) bands(%)

Water 2150 85.02 100 100 99.95

Vegetation 49747 83.75 81.04 81.03 80.99

Barren Land 116639 77.41 73.09 72.79 72.88

Built Up Area 25033 92.67 92.88 92.77 92.06

Overall 193569 81.0987 77.9839 77.7876 77.7355

Table 8.1: SAM Classification efficiency for FLAASH corrected image.

Class name Sample Size Subset-196 PCA-15 PCA-10 PCA-5(Pixels) bands(%) bands(%) bands(%) bands(%)

Water 2150 84.20 99.58 99.58 99.58

Vegetation 49747 83.74 81.88 81.88 81.85

Barren Land 116639 74.77 74.12 74.11 96.35

Built Up Area 25033 84.05 96.49 96.48 73.99

Overall 193569 78.3785 79.2934 79.2836 79.1880

Table 8.2: SAM Classification efficiency for QUAC corrected image.

8.4 Mahalanobis Distance Classifier

In statistics, Mahalanobis Distance is a distance metric, proposed by P.C.Mahalanobis in his

landmark paper in 1936[25]. It is based on correlations between variables by which differ-

ent patterns can be identified and analyzed. It gauges similarity of an unknown sample set to

known one. It differs from Euclidian distance that it takes into account the correlations of the

dataset and is scale variant[26].

Mahalanobis distance is widely used in cluster analysis and classification techniques. In

order to use the Mahalanobis distance to classify a test point as belonging to one of N classes,

one first estimates the covariance matrix of each class, usually based on samples known to be-

long to each class. Then, given a test sample, one computes the Mahalanobis distance to each

class, and classifies the test point as belonging to that class for which the Mahalanobis distance

is minimal.



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8.4.1 Mathematical Background

Formally, the Mahalanobis distance of a multivariate vector x = (x1, x2, x3, .....xN)T from a

group of values with mean= (1, 2, 3, .....N)T and covariance matrixSis defined as

DM(x) =

(x)TS1(x) (8.2)

Mahalanobis Distance or generalized squared interpoint distance for its squared value,

can also be defined as a dissimilarity measure between two random vectors x,y of the same

distribution with same covariance matrixS

d(x, y) =

(x y)TS1(x y) (8.3)

If the covariance matrix is the identity matrix, the Mahalanobis distance reduces to the

Euclidian distance. If the covariance matrix is diagonal, then resulting distance measure is

callednormalized Euclidian distance:

d(x, y) =

N

i=1

(xiyi)2

s2i

(8.4)

wheresiis the standard deviation of thexiand yiover the sample set.

Fig8.5shows classification output for the Mahalanobis distance classification on FLAASH

and QUAC corrected images.

Confusion matrices were then generated for all Mahalanobis Distance classified images to

estimate its efficiency. Ground truth image was taken as reference for generating confusion

matrix with respect to classified output images for 196 band image and 5, 10, 15 band PCA

images. These results were then tabulated into the form of a table as shown below



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(a) Mahalanobis classification for QUAC Correction. (b) Mahalanobis classification for FLAASH Correction.

Figure 8.5: Mahalanobis Distance classified output images.



Water 2150 91.45 84.01 82.39 71.19

Vegetation 49747 86.51 85.40 85.57 84.82

Barren Land 116639 87.17 85.64 80.53 79.95

Built Up Area 25033 88.56 88.60 91.92 86.55

Overall 193569 87.2305 85.9416 83.3208 81.9570

Table 8.3: Mahalanobis Distance Classification efficiency for FLAASH corrected image.



Water 2150 92.61 97.63 97.82 99.21

Vegetation 49747 86.20 85.13 84.86 84.90

Barren Land 116639 86.98 83.12 82.75 81.21

Built Up Area 25033 88.31 92.08 92.49 95.83

Overall 193569 87.0161 84.9569 84.7178 84.2497

Table 8.4: Mahalanobis Distance Classification efficiency for QUAC corrected image.

8.5 Minimum Distance Classifier

The Minimum Distance classification is used in many remote sensing methods like crop species

identification, land pattern identification etc. Minimum Distance classifiers belong to family

of classifiers referred to as sample classifiers. In such classifiers the items classified are groups



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of measurement vectors(e.g. all measurement vectors from agricultural field), rather than indi-

vidual vectors as in more conventional vector classifiers[27].

Specifically in the Minimum Distance classification, a sample, i.e. a group of vectors, is

classified into the class whose known or estimated distribution most closely resembles the es-

timated distribution of the sample to be classified. The measure of resemblance is a distance

measure in the space of distributed functions.

Minimum distance classification resembles what is probably the the oldest and simplest ap-

proach to pattern recognition, namely template matching. In template matching a template

is stored for each class or pattern to be recognized (for e.g. letters of an alphabet and an un-

known pattern (e.g. an unknown letter) is then classified into the pattern class whose template

best fits pattern on basis of some previously defined similarity measure. In minimum distance

classification the templates and unknown patterns are distribution functions and the measure of

similarity used is a distance measure between distribution functions.

Thus, an unknown distribution is classified into the class whose distribution function is near-

est to the unknown distribution in terms of some predetermined distance measure. In practice

the distribution functions involved are usually not known, nor can they be observed directly.

Rather a set of random measurement vectors from each distribution of interest is observed and

classification is based

hyperspectral signal processing to identify land cover pattern.pdf

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