Transcript Selected Hyperspectral Mapping Method

SELECTED HYPERSPECTRAL
MAPPING METHOD
Mirza Muhammad Waqar
Contact:
[email protected]
+92-21-34650765-79 EXT:2257
RG712
Course: Special Topics in Remote Sensing & GIS
Outlines
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Hyperspectral Data
Hyperspectral vs Multispectral Data Analysis
Hyperspectral Mapping Techniques
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Spectral Angle Mapper
Matched Matching
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Spectral Feature Fitting
Binary Encoding (BE)
Complete Linear Spectral Unmixing
Match Filtering
Revision – Hyperspectral Thematic Mapping
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Imaging Spectrometry
Multispectral versus Hyperspectral
Hyperspectral Image Acquisition
Extraction of information from Hyperspectral data
 Preprocessing of Data
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Subset Study Area
Initial Image Quality Assessment
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Visual Individual Band Examination
Visual Examination of Color Composite
Animation
Statistical Individual Band Examination
Radiometric Calibration
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In situ data
Radiosounder
Radiative Transfer based Atmospheric Correction
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DN Value
Radiance
Irradiance
Apparent Reflectance
(Albedo)
5. Reflectance
Revision – Hyperspectral Thematic Mapping
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Selected Atmospheric Correction Models
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Reducing Data Redundancy
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Principal Component Transformation
Minimum Noise Fraction Transformation (MNF)
Endmember Determination
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Flat Field Correction
Internal Average Relative Reflectance (IARR)
Empirical Line Calibration
Pixel Purity Index (PPI)
n-dimensional visualization of endmembers in feature space
Hyperspectral Mapping Method
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Spectral Angle Mapper (SAM)
Hyperspectral Data
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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.
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Landsat
ASTER
MODIS
AVIRIS
Hyperion
Hyperspectral vs. Multispectral Data Analysis
Hyperspectral
Multispectral
Bands
Contiguous each other
Discrete each other
Analysis objectives
Discriminate 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
More reliance on physical and
biophysical models
More reliance on
statistical techniques (ex.
maximum likelihood
classification)
Analysis methods
Multispectral vs Hyperspectral Mapping
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Multispectral Analysis methods are generally
inadequate when applied to hyperspectral data:
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Inefficient:
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Accuracy degradation
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Multispectral methods are too computationally intensive
when applied to high dimensional data
Classification accuracy can actually decrease with the
addition of extra bands that do not contribute meaningful
information content.
Loss of subtle detail
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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
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Atmospheric Correction
Classification and target identification
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Whole pixel method
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Subpixel method
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Spectral Angle Mapper
Spectral Feature Fitting
Complete Linear Spectral Unmixing
Matched Filtering
Others
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Neural network
Decision boundary feature extraction (DBFE)
Spectral Angle Mapper (SAM)
Spectral Angle Mapper (SAM)
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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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n
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(Ti  Ri )
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1
i 1
a  cos 
1
1 
 n 2 2  n 2 2 
   Ti     Ri  
 
  i 1   i 1
a = spectral angle between two spectra
n = number of bands
Ti = reflectance value of band i in the test spectra
Ri = 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 Spectra
C λ = Least Square fit factor
Matched Matching
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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
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Ex. Tetracorder by USGS
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http://speclab.cr.usgs.gov/tetracorder.html
Spectral Feature Fitting (SFF)
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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
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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)
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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:
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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
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Calculate the fractions of endmembers in each
pixel
Endmembers
 Spectrally unique
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surface materials
Similar to fuzzy classification with multispectral
data analysis
Results
 An
abundance image, and
 Membership images
Complete Linear Spectral Unmixing
Matched Filtering
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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.
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
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Spectral feature
fitting
Minral Maps
Spectral Angle
Mapping
Spectral
Unmixing
Questions & Discussion