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4 April 2008 Constrained basis set expansions for target subspaces in hyperspectral detection and identification
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Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown environmental conditions (i.e., atmospheric, illumination, surface temperature, etc.) by specifying a subspace of possible target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image. The subspaces, defined from a set of exemplar spectra, are compactly expanded in singular value decomposition basis vectors or, less commonly, endmember basis spectra, linear combinations of which are used to fit the image data. In the present study we compared detection performance in the thermal infrared using several different constrained and unconstrained basis set expansions of low-dimensional subspaces, including a method based on the Sequential Maximum Angle Convex Cone (SMACC) endmember algorithm. Constrained expansions were found to provide a modest improvement in algorithm robustness in our test cases.
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S. Adler-Golden, J. Gruninger, and R. Sundberg "Constrained basis set expansions for target subspaces in hyperspectral detection and identification", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 696602 (4 April 2008);

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