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[Colloquium] Sparse Matrix Transform for Hyperspectral Image Processing

December 2, 2011

Watch Colloquium: 

M4V file (645 MB)

  • Date: Friday, December 2, 2011 
  • Time: 12:00 pm — 12:50 pm
  • Place: Centennial Engineering Center 1041

James Theiler Los Alamos National Laboratory

Many problems in image processing require that a covariance matrix be accurately estimated, often from a limited number of data samples. This is particularly challenging for hyperspectral imagery, where the number of spectral channels can run into the hundreds. The Sparse Matrix Transform (SMT) provides a parsimonious, computation-friendly, and full-rank estimator of covariance matrices. But unlike other covariance regularization schemes, which deal with the eigenvalues of a sample covariance, the SMT works with the eigenvectors. This talk will describe the SMT and its utility for a range of problems that arise in hyperspectral data analysis, including weak signal detection, dimension reduction, anomaly detection, and anomalous change detection.


Bio: James Theiler finished his doctoral dissertation at Caltech in 1987, with a thesis on statistical and computational aspects of identifying chaos in time series. He followed a nonlinear trajectory to UCSD, MIT Lincoln Laboratory, Los Alamos National Laboratory, and the Santa Fe Institute. His interests in statistical data analysis and in having a real job were combined in 1994, when he joined the Space and Remote Sensing Sciences Group at Los Alamos. In 2005, he was named a Los Alamos Laboratory Fellow. His professional interests include statistical modeling, image processing, remote sensing, and machine learning. Also, covariance matrices.