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20 May 2011 High spatial resolution hyperspectral spatially adaptive endmember selection and spectral unmixing
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Linear spectral unmixing and endmember selection are two of the many tasks that can be accomplished using hyperspectral imagery. The quality of the unmixing results depends on an accurate estimate of the number of endmembers used in the analysis. Too many estimated endmembers produce over fitting of the spectral unmixing results; too few estimated endmembers produce spectral unmixing results with large residual errors. Several statistical and geometrical approaches have been developed to estimate the number of endmembers, but many of these approaches rely on using the global dataset. The global approach does not take into consideration local endmember variability, which is of particular interest in high-spatial resolution imagery. Here, the number of endmembers within local image tiles is estimated by using a novel, spatially adaptive approach. Each pixel is unmixed using the locally identified endmembers and global abundance maps are generated by clustering these locally derived endmembers. Comparisons are made between this new approach and an established global method that uses PCA to estimate the number of endmembers and SMACC to identify the spectra. Multiple images with varying spatial resolution are used in the comparison of methodologies and conclusions are drawn based on per-pixel residual unmixing errors.
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Kelly Canham, Ariel Schlamm, Bill Basener, and David Messinger "High spatial resolution hyperspectral spatially adaptive endmember selection and spectral unmixing", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481O (20 May 2011);

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