SELECTED HYPERSPECTRAL MAPPING METHOD

Course: Special Topics in Remote Sensing & GIS

Mirza Muhammad WaqarContact:

mirza.waqar@ist.edu.pk+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