Spectral Mixture Analysis - New Strategies for the Analysis of Multispectral Data
Instrument noise, spectral contrast among scene components and variability of spectral scene components are not explicitly evaluated as part of classification and mapping efforts using multispectral images. Yet changes in these factors directly affect mapping accuracy. An analytical framework is proposed such that these factors can be quantified within the context of spectral mixture analysis (SMA). In applying these analyses to an AVBRIS image of Owens Valley, California, U.S.A., we find that the greatest uncertainty in abundance estimates arises from spectral variability in endmembers. Spectral variability in any endmember results in abundance uncertainty of all endmembers. We propose an analytical strategy that subsets an image into regions of lowest spectral dimensionality to minimize uncertainties and to maximize detection of new materials.
KeywordsMultispectral Image Abundance Estimate Instrumental Noise Spectral Variability Linear Mixture
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