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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)
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)
—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)
—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)
—We develop a vegetation mapping method using long-wave hyperspectral imagery and apply it to landmine detection. The novel aspect of the method is that it makes use of emissivity skewness. The main purpose of vegetation detection for mine detection is to minimize false alarms. Vegetation, such as round bushes, may be mistaken as mines by mine detection(More)