HYPERSPECTRAL ENDMEMBER DETECTION AND BAND SELECTION USING BAYESIAN METHODS By ALINA ZARE A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy HYPERSPECTRAL ENDMEMBER DETECTION AND BAND SELECTION USING BAYESIAN METHODS By Alina Zare December 2008 Chair: Paul Gader Major: Computer Engineering Four methods of endmember detection and spectral unmixing are described. The methods determine endmembers and perform spectral unmixing while simultaneously determining the number of endmembers, representing endmembers as distributions, partitioning the input data set into several convex regions, or performing hyperspectral band selection. Few endmember detection algorithms estimate the number of endmembers in addition to determining their spectral shape. Also, methods which treat endmembers as distributions or treat hyperspectral images as piece-wise convex data sets have not been previously developed. A hyperspectral image is a three-dimensional data cube containing radiance values collected over an area (or scene) in a range of wavelengths. Endmember detection and spectral unmixing attempt to decompose a hyperspectral image into the pure separate and individual spectral signatures of the materials in a scene, and the proportions of each material at every pixel location. Each spectral pixel in the image can then be approximated by a convex combination of proportions and endmember spectra. The first method, the Sparsity Promoting Iterated Constrained Endmembers (SPICE) algorithm, incorporates sparsity-promoting priors to estimate the number of endmembers. The algorithm is initialized with a large number of endmembers. The sparsity promotion process drives all proportions of some endmembers to zero. These endmembers can be removed by SPICE with no effect on the error incurred by representing the image