Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral ImagerySPIE, 2004 - Computer algorithms |
Contents
The extension of endmember extraction to multispectral scenes 542502 | 15 |
A proof of the NFINDR algorithm for the automated detection of endmembers in | 31 |
Error analysis in hyperspectral unmixing 542504 | 42 |
Copyright | |
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224-channel calculations 6-channel calculations absorption abundance maps Airborne algorithm Algorithms and Technologies alunite analysis applied approach AVIRIS background basis vectors basis vectors calibration channel classification clusters computed constraints convex cone convex set covariance matrix Cuprite data set defined detector dimensionality distribution edge detection emissivity end-images end-spectra endmember pairs endmember selection equation error estimate expansion coefficients extreme vectors function H₁ HSI data hyperspectral data hyperspectral image hyperspectral imagery IEEE illustrated in Figure infrared LANDSAT linear mixing model matched filter material MaxD maximum measured mixture model MODTRAN multispectral noise number of endmembers oblique projection optimal parameters performance pixel pixel models pixel spectra plume QuickBird radiance region registration Remote Sensing residual ROC curves sample scene segmentation sensor simplex space spectral bands spectrum SPIE Vol statistics subpixel subset subspace target basis vectors target detection techniques Technologies for Multispectral temperature transform Ultraspectral Imagery unmixing Wavelength