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—A Metropolis-within-Gibbs Sampler for Piece-wise Convex hyperspectral Unmixing and Endmember extraction (S-PCUE), is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often non-convex. Furthermore, in standard endmember extraction and(More)
—A new hyperspectral endmember detection method that represents endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions and proportion values for each convex region is presented. Spectral unmixing methods that treat endmembers as distributions or hyperspectral(More)
Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis. To develop accurate spectral unmixing and endmember estimation methods, a number(More)
An algorithm, Bilinear SPICE (BISPICE), for simultaneously estimating the number of endmembers, the endmembers, and proportions for a bilinear mixing model is derived and evaluated. BISPICE generalizes the SPICE algorithm for linear mixing. The proportion estimation steps of SPICE and BISPICE are similar. However, the endmember updates, one novel aspect of(More)
—Hyperspectral unmixing estimates the proportions of materials represented within a spectral signature. The overwhelming majority of hyperspectral unmixing algorithms are based entirely on the spectral signatures of each individual pixel and do not incorporate the spatial information found in a hyper-spectral data cube. In this work, a spectral unmixing(More)
2 To Mom and Dad 3 ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Paul Gader, for all of his guidance, support and the numerous opportunities he provided me throughout my studies and research. I would also like to thank my committee members, Dr. Additionally, thank you to my many former and current labmates. I am particularly grateful to Jeremy(More)
—A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piece-wise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of endmembers and abundances are found using an iterative fuzzy(More)