Selected Hyperspectral Mapping Method
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Transcript of Selected Hyperspectral Mapping Method
SELECTED HYPERSPECTRAL MAPPING METHOD
Course: Special Topics in Remote Sensing & GIS
Mirza Muhammad WaqarContact:
[email protected]+92-21-34650765-79 EXT:2257
RG712
Outlines
Hyperspectral Data Hyperspectral vs Multispectral Data Analysis Hyperspectral Mapping Techniques
Spectral Angle Mapper Matched Matching
Spectral Feature Fitting Binary Encoding (BE)
Complete Linear Spectral Unmixing Match Filtering
Revision – Hyperspectral Thematic Mapping
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
1. DN Value2. Radiance3. Irradiance4. Apparent Reflectance
(Albedo)5. Reflectance
Revision – Hyperspectral Thematic Mapping
Selected Atmospheric Correction Models Flat Field Correction Internal Average Relative Reflectance (IARR) Empirical Line Calibration
Reducing Data Redundancy Principal Component Transformation Minimum Noise Fraction Transformation (MNF)
Endmember Determination Pixel Purity Index (PPI) n-dimensional visualization of endmembers in feature space
Hyperspectral Mapping Method Spectral Angle Mapper (SAM)
Hyperspectral Data
In order to be considered a specific data as hyperspectral, three conditions should be satisfied.
Multiple bands High spectral resolution (i.e. narrowness of each band) Contiguity of bands.
Landsat ASTER MODIS AVIRIS Hyperion
Hyperspectral vs. Multispectral Data Analysis
Hyperspectral Multispectral
Bands Contiguous each other Discrete each other
Analysis objectivesDiscriminate material among various earth surface features
Categorize features
Signal-to-noise ratio
Lower (i.e. tendency of more noise)
Higher
Atmospheric interference
More susceptible Less susceptible
Analysis methodsMore reliance on physical and
biophysical models
More reliance on statistical techniques (ex.
maximum likelihood classification)
Multispectral vs Hyperspectral Mapping
Multispectral Analysis methods are generally inadequate when applied to hyperspectral data: Inefficient:
Multispectral methods are too computationally intensive when applied to high dimensional data
Accuracy degradation Classification accuracy can actually decrease with the
addition of extra bands that do not contribute meaningful information content.
Loss of subtle detail The standard multispectral pattern recognition methods
ultimately equate variance with information, which often results in subtle spectral variations being lost in the noise.
Hyperspectral Mapping Techniques
Atmospheric Correction Classification and target identification
Whole pixel method Spectral Angle Mapper Spectral Feature Fitting
Subpixel method Complete Linear Spectral Unmixing Matched Filtering
Others Neural network Decision boundary feature extraction (DBFE)
Spectral Angle Mapper (SAM)
Spectral Angle Mapper (SAM)
SAM compares test image spectra to a known reference spectra using the spectral angle between them.
This method is not sensitive to illumination since the SAM algorithm uses only the vector direction and not the vector length.
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RT
RTa
a = spectral angle between two spectran = number of bandsTi = reflectance value of band i in the test spectraRi = reflectance value of band i in the reference spectra
Continuum Removal
A continuum is a mathematical function used to isolate a particular absorption feature for analysis (Clark and Roush, 1984; Kruse et al, 1985; Green and Craig, 1985).
LC= Continuum Removed Spectra using library spectra
L = Library SpectraC λ = Least Square fit factor
Matched Matching
Spectral Feature Fitting (SFF): A least-squares technique. SFF is an absorption-feature-based methodology. The reference spectra are scaled to match the image spectra after continuum removal from both data sets. (e.g. Tetracorder)
Examines absorption features Depth Shape
Ex. Tetracorder by USGS http://speclab.cr.usgs.gov/tetracorder.html
Spectral Feature Fitting (SFF)
Where Rb is reflectance in band center Rc is reflectance in continuum at band center
Use specific bands to search for individual features and estimate a relative concentration based on band depth. A. First generate a continuum-removed spectrum for a specific feature in
order to compare it with library spectra and image-derived spectra. B. Convolve library spectra with spectral response of sensor to generate
an estimate of image derived reflectance spectra (i.e., assumes some form of atmospheric inversion has been applied to image data).
Matched Matching
Binary Encoding (BE): The binary encoding classification technique encodes the data and end member spectra into 0s and 1s based on whether a band falls below or above the spectrum mean. An exclusive OR function is used to compare each encoded reference spectrum with the encoded data spectra and a classification image produced.
Binary Encoding (BE)
Compute spectral mean of a sample (pixel) Assign a 1 to bands equal or greater than mean and 0 to
those less than mean. Do the same for reference (e.g. spectral library) spectra. Compare the pattern as a measure of similarity. Compute spectral mean Rm of sample (pixel) over a local
waveband of interest Assign a 1 to bands equal or greater than mean and 0 to
those less than mean: If R( ) ≥ λ Rm assign a “1” If R( ) < λ Rm assign a “0”
Binary Encoding (BE)
Linear vs Non-Linear Mixing
Linear Mixing
Complete Linear Spectral Unmixing
Calculate the fractions of endmembers in each pixel
Endmembers Spectrally unique surface materials
Similar to fuzzy classification with multispectral data analysis
Results An abundance image, and Membership images
Complete Linear Spectral Unmixing
Matched Filtering
Partial unmixing technique Originally developed to compute
abundances of targets that are relatively rare in the scene.
Matched Filtering “filters” the input image for good matches to the chosen target spectrum by maximizing the response of the target spectrum within the data and suppressing the response of everything else.
One potential problem with Matched Filtering is that it is possible to end up with false positive results.
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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
Questions & Discussion