Digital Imaging and Remote Sensing Laboratory Real-World Stepwise Spectral Unmixing Daniel Newland Dr. John Schott Digital Imaging and Remote Sensing Laboratory Center for Imaging Science May 7, 1999

Digital Imaging and Remote Sensing Laboratory OutlineOutline Objective Unmixing Background Data and Preparation Results Conclusions

Digital Imaging and Remote Sensing Laboratory ObjectiveObjective Use real data to verify a stepwise spectral unmixing routine tested only on synthetic data –Compare to traditional spectral unmixing –Compare to hierarchical spectral unmixing Hyperspectral DataMaterial Fraction Maps

Digital Imaging and Remote Sensing Laboratory OutlineOutline Objective Unmixing Background Data and Preparation Results Conclusions

Digital Imaging and Remote Sensing Laboratory Operating Scenario Remote sensing by airborne or spaceborne imagers Finite flux reaching sensor causes spatial-spectral resolution trade-off Hyperspectral data has hundreds of bands of spectral information Spectrum characterization allows subpixel analysis and material identification

Digital Imaging and Remote Sensing Laboratory Spectral Mixture Analysis Assumes reflectance from each pixel is caused by a linear mixture of subpixel materials Mixed Spectrum Example

Digital Imaging and Remote Sensing Laboratory Mixed Pixels and Material Maps Input Image Red Fraction Map Green Fraction Map UNMIXED PURE MIXED

Digital Imaging and Remote Sensing Laboratory i = 1 … k Constraint Conditions Unconstrained: Partially Constrained: Fully Constrained: Traditional Linear Unmixing

Digital Imaging and Remote Sensing Laboratory Unmixes broad material classes first Proceeds to a group’s constituents only if the unmixed fraction is greater than a given threshold Hierarchical Linear Unmixing Example Materials Hierarchy Full Library Concrete Metal Water Deciduous Trees Coniferous Trees Grass

Digital Imaging and Remote Sensing Laboratory Stepwise Unmixing Employs linear unmixing to find fractions Uses iterative regressions to accept only the endmembers that improve a statistics-based model Shown to be superior to classic linear method –Has better accuracy –Can handle more endmembers Quantitatively tested only on synthetic data

Digital Imaging and Remote Sensing Laboratory Performance Evaluation Compare squared error from traditional, stepwise and hierarchical methods Visually assess fraction maps for accuracy Error Metric:

Digital Imaging and Remote Sensing Laboratory OutlineOutline Objective Unmixing Background Data and Preparation Results Conclusions

Digital Imaging and Remote Sensing Laboratory Data and Preparation Used HYDICE collection over the ARM site –210 bands around 10nm in width –Covers wavelengths of microns –Spatial resolution of 1.75 meters per pixel Processed original scene to generate unmixing input –Spatial averaging to form mixed pixels –Spectral subset to remove noise Constructed material library and truth map

Digital Imaging and Remote Sensing Laboratory HYDICE Scene Original 320 x 320 Convolved 80 x 80

Digital Imaging and Remote Sensing Laboratory Atmospheric Attenuation

Digital Imaging and Remote Sensing Laboratory Atmospheric Effects Band microns Road Pixel Vegetation Pixel

Digital Imaging and Remote Sensing Laboratory Endmember Selection Endmembers are simply material types –Broad classification: road, grass, trees… –Fine classification: dry soil, moist soil... Used image-derived endmembers to produce spectral library –Average reference spectra from “pure” sample pixels –Chose 18 distinct endmembers

Digital Imaging and Remote Sensing Laboratory Endmember Listing Strong Road Weak Road Panel 2k Panel 3k Panel 5k Panel 8k Panel 14k Panel 17k Panel 25k Spectral Panel Parking Lot Trees Strong Vegetation Medium Vegetation Weak Vegetation Strong Cut Vegetation Medium Cut Vegetation Weak Cut Vegetation False-Color IR

Digital Imaging and Remote Sensing Laboratory Materials Hierarchy Grouped similar materials into 3-level hierarchy – Level 1 – Level 2 – Level 3

Digital Imaging and Remote Sensing Laboratory Truth Map Creation Realistic classification required automated procedure Tested classification routines available in ENVI Chose Minimum Distance to the Mean classifier

Digital Imaging and Remote Sensing Laboratory Truth Map False-Color IR Classified Scene

Digital Imaging and Remote Sensing Laboratory Truth Detail Test Site Trees Parking Lot

Digital Imaging and Remote Sensing Laboratory Tools for Analysis Data processed with ENVI and IDL Three unmixing routines written in IDL IDL support programs

Digital Imaging and Remote Sensing Laboratory OutlineOutline Objective Unmixing Background Data and Preparation Results Conclusions

Digital Imaging and Remote Sensing Laboratory Truth Fraction Maps Labels Fractions

Digital Imaging and Remote Sensing Laboratory Linear Unmixing Linear Truth

Digital Imaging and Remote Sensing Laboratory Hierarchical Unmixing Hierarchical Truth

Digital Imaging and Remote Sensing Laboratory Stepwise Unmixing Stepwise Truth

Digital Imaging and Remote Sensing Laboratory Fraction Maps Material Truth Linear Hierarch. Stepwise Panel 3k Uncut Mid Vegetation Cut Weak Vegetation

Digital Imaging and Remote Sensing Laboratory Linear Color Maps

Digital Imaging and Remote Sensing Laboratory Hierarchical Color Maps

Digital Imaging and Remote Sensing Laboratory Stepwise Color Maps

Digital Imaging and Remote Sensing Laboratory Histogram Comparison Linear Hierarchical Stepwise

Digital Imaging and Remote Sensing Laboratory Squared Error Results

Digital Imaging and Remote Sensing Laboratory Hierarchical Results

Digital Imaging and Remote Sensing Laboratory OutlineOutline Objective Unmixing Background Data and Preparation Results Conclusions

Digital Imaging and Remote Sensing Laboratory ConclusionsConclusions Linear unmixing does poorly, forcing fractions for all materials Hierarchical approach performs better but requires extensive user involvement Stepwise routine succeeds using adaptive endmember selection without extra preparation

Digital Imaging and Remote Sensing Laboratory Special Thanks Dr. John Schott Daisei Konno Lee Sanders Francois Alain

Digital Imaging and Remote Sensing Laboratory Questions?Questions? False-Color IR Stepwise Unmixed Fraction Maps