Lab Home | Phone | Search
	Center for Nonlinear Studies
	Home  ▶ People   Current   Executive Committee   Postdocs   Visitors   Students   Research   Publications  ▶ Conferences   Workshops   Sponsorship  ▶ Talks   Seminars   Postdoc Seminars Archive   Quantum Lunch   Quantum Lunch Archive   P/T Colloquia   Archive   Ulam Scholar     Postdoc Nominations  ▶ Student Requests   Student Program  ▶ Visitor Requests   Description   Past Visitors  ▶ Services   General     History of CNLS     Maps, Directions   CNLS Office   T-Division   LANL				Wednesday, August 24, 20113:00 PM - 4:00 PMCNLS Conference Room (TA-3, Bldg 1690) Seminar Target Detection in Hyperspectral Imagery with Singular Covariance Matrices Stanley R. RotmanBen-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