thematic information extraction – hyperspectral image analysis course: special topics in remote...
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THEMATIC INFORMATION EXTRACTION – HYPERSPECTRAL
IMAGE ANALYSIS
Course: Special Topics in Remote Sensing & GIS
Mirza Muhammad WaqarContact:
[email protected]+92-21-34650765-79 EXT:2257
RG712
Outlines
Imaging Spectrometry Multispectral versus Hyperspectral Hyperspectral Image Acquisition Extraction of information from Hyperspectral data
Preprocessing of Data Subset Study Area Initial Image Quality Assessment
Visual Individual Band Examination Visual Examination of Color Composite Animation Statistical Individual Band Examination
Radiometric Calibration In situ data Radiosounder Radiative Transfer based Atmospheric Correction
Selected Atmospheric Correction Models Reducing Data Redundancy Endmember Determination Hyperspectral Mapping Method
Imagining Spectrometry
Imagining spectrometry is defined as the simultaneous acquisition of images in many relatively
narrow contiguous and/or noncontiguous spectral bands throughout the ultraviolet, visible, and infrared portions of
electromagnetic spectrum”
Most multispectral 3 to 10 spectral bands For Example
Landsat (MSS, TM & ETM+) ALOS SPOT (HRV) IKNOS QuickBird Orbview Digital Globe Worldview Aerial phtography
Hyperspectral At least 10 or more
spectral bands Example includes:
MODIS MERIS (Envisat) MOS (IRS-3P, India) Hyperion (EO-1) CHRIS (PROBA, ESA) AVIRIS (JPL, NASA) DAIS 7915 (DLR) HYDICE (NRL, USA)
Hyperspectral vs Multispectral
1. Selection of appropriate Software Package
2. Image Quality Assessment
3. Radiometric Correction
4. Geometric Correction
5. Dimensionality Reduction
6. Selection of end members
7. Mapping methods
Extraction of Information from Hyperspectral Data
Selection of appropriate Software Package
The analysis of hyperspectral data usually required selection of appropriate digital image processing software package e.g.
ENVI, the Environment for Visualizing Images ERDAS Imagine IDRISI PCI Geomatica
Accuracy Assessment
Geometric Correction
Band Selection
Atmospheric Correction
End Member Selection
Classification SAM
State the nature of the information extraction problem
1. Specify the geographical ROI2. Define the classes or biological
materials of interestAcquire appropriate remote sensing and initial ground ref data
1. Select RS data based on the following criteria
1. RS system consideration:1. Spatial, spectral, temporal &
radiometric resolution2. Environmental considerations:
1. Atmospheric, soil moisture, phonological cycle, etc.
3. Obtain initial ground reference data based on:
1. A priori knowledge of the study area
Process hyperspectral data to extract thematic information
1. Subset the study area 2. Conduct initial image quality assessment:
1. Visual individual band examination2. Visual examination of color composite
images3. Animation 4. Statistical individual band examination
(S/N ratio)3. Radiometric Correction
1. Collect necessary in situ spectroradiometer data (if possible)
2. Collect in situ or environmental data (e.g. using radio sounder)
3. Perform pixel by pixel correction (e.g. ACORN)
4. Perform pixel by pixel spectral polishing5. Empirical Line Calibration
4. Geometric Correction / Rectification1. Use onboard navigation and engineering
data (GPS & INS Data)2. Nearest neighbor resampling
5. Reduce the dimensionality of hyperspectral data
1. Minimum Noise Fraction (MNF) transformatoin
6. End Member determination – locate pixels which relatively pure spectral characteristics:
1. Pixel Purity Index2. N-dimensional end member visualization
7. Method of mapping and matching using hyperspectral data:
1. Spectral Angle Mapper (SAM)2. Subpixel Classification (Linear Spectral
Mixing3. Spectroscopic library matching
techniques4. Matched filter or mixture-tuned matched
filter5. Indices developed for use with
hyperspectral data6. Derivative spectroscopy
Perform accuracy assessment1. Select method
1. Qualitative confidence building2. Statistical measurement
2. Determine number of observations required by class
3. Select sampling scheme4. Obtain ground reference data5. Create and analyze error matrix:
1. Uni-varitae and multivariate statistical analysis
Accept or reject previously stated hypothesis.
Distribute result if accuracy is acceptable.
Initial Image Quality Assessment
To assess the data quality, suitable distortion measures relevant to end-user applications are required.
1. Visual Individual Band Examination2. Visual Examination of Color Composite Images 3. Animation4. Statistical Individual band Examination
1. Visual Individual Band Examination
Many bands of hyperspectral data contain bad data values or they lie in the absorption
window. Such bands must be excluded from the analysis
because these reduce the contrast of data.
Bad Data If a band contain data values (e.g. -9999) Line dropout (an entire line has a value of -9999)
1. Visual Individual Band Examination
Reference: Jun Huang, Helle Wium, Karsten B. Qvist, Kim H. Esbensen, Multi-way methods in image analysis—relationships and applications, Chemometrics and Intelligent Laboratory Systems, Volume 66, Issue 2, 28 June 2003, Pages 141-158, ISSN 0169-7439, 10.1016/S0169-7439(03)00030-3. (http://www.sciencedirect.com/science/article/pii/S0169743903000303) Keywords: Multivariate Image Analysis (MIA); Multi-way methods; Unfolding; Image; PCA/PLS; PARAFAC; Tucker3; N-PLS; 2-D FFT
2. Visual Examination of Color Composites
Can be use to check The individual bands are co-align Contain spectral information of value
Such examination provide Valuable quantitative information
About the individual scenes and bands in the hyperspectral data
3. Animation
Most hyperspectral image analysis software have image animation function. E.g. every 5 second a new band will be displayed
Examination of hyperspectral data in this way allows: Identify individual bands that have serious
atmospheric attenuation Determine if any mis-registration of band exist
4. Statistical Individual Band Examination
It includes examination of uni-variate statistics of individual band Mean Median Mode Standard Deviation Range
To detection absorption feature Noise level must be smaller than the absorption level.
Radiometric Calibration
To use hyperspectral data properly It is generally accepted that the data must be radiometrically
corrected.
This process normally involves transforming the hyperspectral data from at-sensor radiance, to scaled surface reflectance.
This allows image spectra quantitatively comparable with the in situ spectra Obtained using handheld spectroradiometer.
Digital Number (DN)
Digital Number (DN) – the unitless integer that a satellite uses to record relative amounts of radiance (e.g. 0 – 255 where 0 = no radiance and 255 = some maximum amount of radiance that the sensor is sensitive to). Each image pixel has one DN for each band.
Note that DNs are just an index of radiance and don’t have physical units of radiance.
Radiance
Radiance (L) – the physical amount of light received at a particular place in this case a satellite (watts/m2/sr).
Irradiance
Irradiance (E) – the amount of incoming light from the sun (either at the ground (E) or at the top of the atmosphere (E0 or TOA) (watts/m2).
Reflectance
Reflectance (r) – the amount of light that reflects off of something divided by the amount of incoming light (often given as a decimal fraction or a percent). Also called surface reflectance
Apparent Reflectance (Albedo), Reflectance
Apparent Reflectance (Albedo) "Albedo is defined as the fraction of incident
radiation that is reflected by a surface.
Reflectance While reflectance is defined as this same
fraction for a single incidence angle, albedo is the directional integration of reflectance over all sun-view geometries."
1. In Situ Data Collection
For Hyperspectral Image Analysis, It is always desirable to obtain handheld in situ
spectrometer measurement on the ground at Approximately same time as the remote sensing over
flight Otherwise same time of day
That cover the spectral range as the hyperspectral imaging system
Spectrometer used in the field must be calibrated with reference spectrometer (laboratory spectrometer)
2. Radiosondes
Radiosonders can provide valuable information about the atmosphere Tempearture Pressure Relative Humidity Wind Speed Ozone Wind Direction
Radiosonder data helps in radiometeric correction.
3. Radiative Transfer-based Atmospheric Correction
As atmosphere is variable through the scene. Ideally, the analyst knows the exact nature of
atmospheric characteristics over each pixel. Barometric Pressure Water vapor Amount of atmospheric molecules (Scattering)
Remotely sensing – derived radiance data in very selective bands can be used for pixel by pixel atmospheric correction.
Behaviour of Atmospheric Gases
Following seven gases do produce observable absorption in the remotely sensed imagery.1. Water vapour, H2O
2. Carbon Dioxide, CO2
3. Ozone, O3
4. Nitrous Oxide, N2O
5. Methane, CH4
6. Carbon Monoxide, CO
7. Oxygen O2
Band-by-Band Spectral Polishing
Even after atmospheric correction there exist noise in spectra Which is due to sensor system anomilies Limited accuracy of
Standards Measurements Models Calibrations
Spectral polishing is used to remove such errors. EFFORT (Empirical Flat Field Optimal Reflectance
Transformation)
Spectral Polishing - EFFORT
Input Parameters Atmospherically corrected data In situ spectroradiometer spectra
In situ spectral reflectance measurements are sometimes referred to as “reality boost spectra”.
Note: Before applying EFFORT
Mask any invalid data from each band Identify the bad bands and mask these from analysis Avoid wavelength ranges that contain noise such as 1.4 µm and 1.9 µm
water vapour absorption band.
1. Flat Field Correction
2. Internal Average Relative Reflectance (IARR)
3. Empirical Line Calibration
Selected Atmospheric Correction Models
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Flat Field Correction
The Flat Field Correction method normalizes images to an area of known “flat” reflectance (Goetz and
Srivastava, 1985; Roberts et al., 1986).
The average AVIRIS radiance spectrum from the ROI is used as the reference spectrum, which is then divided into the spectrum at each pixel of the image.
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Internal Average Relative Reflectance (IARR)
Used to convert raw DN values to relative reflectance. This is done by dividing each pixel spectrum by the overall average spectrum.
The IARR calibration method normalizes images to a scene average spectrum.
Apparent reflectance is calculated for each pixel of the image by dividing the reference spectrum into the spectrum for each pixel.
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This is particularly effective for reducing imaging spectrometer data to relative reflectance in an area where no ground measurements exist
and little is known about the scene (Kruse et al., 1985; Kruse, 1988).
Internal Average Relative Reflectance (IARR)
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Empirical Line Calibration
The Empirical Line correction method forces image data to match selected field reflectance spectra (Roberts et al.,
1985; Conel et al., 1987; Kruse et al., 1990).
This method based on a model that is derived from the regression of in situ spectroradiometer
measurements at specific location
For more detail read: Third Edition – Introductory Digital Image Processing: A Remote Sensing Perspective by John R. Jensen Chapter 6
1. Principal Component Transformation
2. Minimum Noise Fraction Transformation (MNF)
Reducing the Dimensionality of Hyperspectral Data
Data Dimensionality
The number of spectral bands associated with a remote sensing system is referred to as its data dimensionality.
Orbview: 4 bands Landsat: 7 bands Worldview: 8 bands MODIS: 36 bands AVIRIS: 224 bands
Number of Bands
Com
plex
ity
/ Pr
oces
sing
Data Dimensionality: Multispectral Data
Statistical measures Optimum Index Factor (OIF) Principal Component Analysis (PCA)
These techniques have been used for data dimensionality reduction for multispectral data.
These methods are not significant for reducing hyperspectral data dimensionality.
Principal Component Transformation
Principal components analysis is a method in which original data is transformed into a new set of data which may better capture the essential information.
Often some variables are highly correlated such that the information contained in one variable is largely
a duplication of the information contained in another variable.
Instead of throwing away the redundant data principal components analysis condenses the information
in intercorrelated variables into a few variables, called principal components.
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Minimum Noise Fraction Transformation (MNF)
Hyperspectral Imaging generates vast volumes of data.
100s or more bands might not be necessary to identify and separate the surface materials of interest to a particular study.
Furthermore, some bands might contain more noise than others, making them more of a detriment than an aid to the analysis.
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Eliminating noise and reducing the spectral dimensionality of the data are the goals of Principal Component Analysis (PCA) Minimum Noise Fraction Transformation (MNF).
Information contained in individual hyperspectral bands may be, in some regions of the spectrum, highly redundant.
Minimum Noise Fraction Transformation (MNF)
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The many redundant bands may be collapsed into a much smaller set of MNF bands
without losing the critical information needed to differentiate or identify surface materials.
Furthermore, the noise can also be identified and eliminated using the same methods.
The MNF is used to determine the true or inherent dimensionality of the hypserpsectral data.
To identify and segregate noise in the data To reduce the computation time
Minimum Noise Fraction Transformation (MNF)
The MNF applies two cascaded PCAs. First transformation decorrelate and rescales noise
in the data Result bands have unit variance and no band to band
correlation Second transformation is a standardize PCA, this
results in Coherent MNF eigenimages that contain useful
information Noise dominated MNF eigenimages
Minimum Noise Fraction Transformation (MNF)
Both the eignvalues and output eignimages are used to determine the true dimensionality of the data.
1. How many eignimages should we select for analysis?2. What should be the threshold for eignvalues?
MNF output bands that contain useful information usually have engine value greater than that of noisy
bands.
Minimum Noise Fraction Transformation (MNF)
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Note
MNF results are applicable to that particular dataset or others with very consistent and similar spectral characteristics.
If a project involves many hyperspectral images collected over a large area, the MNF results from one image or set of images may not apply to others in the project.
1. Pixel Purity Index (PPI)
2. n-dimensional visualization of endmembers in feature space
Endmember Determination
Endmember Determination
The primary goal of most of hyperspectral analysis is to identify the
Physical, Chemical Properties of materials found within the IFOV of the sensor system.
The major materials found within hyperspectral image are called endmembers.
These represents relatively pure materials Water Asphalt Concrete Healthy grass
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Pixel Purity Index Mapping (PPI)
Imagine how much more difficult it would be to identify appropriate pixels or groups of pixels with ideal hyperspectral signatures.
Use Pixel Purity Index (PPI) to find the most spectrally pure (extreme) pixels in multispectral and hyperspectral images.
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Pixel Purity Index (PPI) to find the most spectrally pure (extreme) pixels in multispectral and hyperspectral images.
PPI is a rigorous mathematical method of determining the most spectrally pure pixels. By repeatedly projecting n-dimensional scatter plots of
the MNF images. PPI simply identify the most pure pixel. It is difficult to label the type of endmember at this stage
Pixel Purity Index Mapping (PPI)
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Hyperspectral Data Acquisition
Raw Radiance Data
Spectral Calibration
At-Sensor Spectrally Calibrated Radiance
Spatial Pre-Processing and Geocoding
Radiometrically and Spatially processed radiance image
Atmospheric Correction, solar irradiance correction
Geocoding reflectance image
Feature Mapping
Data analysis for feature mapping
Absorption band characterization
Spectral feature fitting
Spectral Angle Mapping
Spectral Unmixing
Minral Maps