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—A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspec-tral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing(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)
—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)
—A hyperspectral pixel is generally composed of a relatively small number of endmembers. Several unmixing methods have been developed to enforce this concept through sparsity promotion or piece-wise convex mixing models. Piece-wise convex un-mixing methods often require as parameters the number of end-members and the number of sets of endmembers needed.(More)
—A compressive sensing framework is described for hyperspectral imaging. It is based on the widely used linear mixing model, LM M , which represents hyperspectral pixels as convex combinations of small numbers of endmember (material) spectra. The coefficients of the endmembers for each pixel are called proportions. The endmembers and proportions are often(More)