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Wednesday, August 24, 2011
3:00 PM - 4:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

Target Detection in Hyperspectral Imagery with Singular Covariance Matrices

Stanley R. Rotman
Ben-Gurion University of the Negev Beer-Sheva, ISRAEL

Accurate covariance matrix estimation for high dimensional data can be a difficult problem; nevertheless it is needed for good target acquisition performance in hyperspectral data. In this talk we will investigate two methods to give a sufficient approximation for the covariance matrix while only using a small number of neighboring pixels. The first is the QLRX (Quasilocal Covariance Matrix RX algorithm) that uses the eigenvectors of a global set of points, coming from a non-stationary distribution, but eigenvalues of the local neighborhood. The second method is the SMT (Sparce Matrix Transform) that performs a set of K Givens rotations to estimate the covariance matrix. We will compare results from target acquisition that are based on both of these methods. An improvement for the SMT algorithm is suggested.

Host: James Theiler, jt@lanl.gov, 665-5682