Title :
Sparse Matrix Transform for Hyperspectral Image Processing
Author :
Theiler, James ; Cao, Guangzhi ; Bachega, Leonardo R. ; Bouman, Charles A.
Author_Institution :
Space & Remote Sensing Group, Los Alamos Nat. Lab., Los Alamos, NM, USA
fDate :
6/1/2011 12:00:00 AM
Abstract :
A variety of problems in remote sensing require that a covariance matrix be accurately estimated, often from a limited number of data samples. We investigate the utility of several variants of a recently introduced covariance estimator-the sparse matrix transform (SMT), a shrinkage-enhanced SMT, and a graph-constrained SMT-in the context of several of these problems. In addition to two more generic measures of quality based on likelihood and the Frobenius norm, we specifically consider weak signal detection, dimension reduction, anomaly detection, and anomalous change detection. The estimators are applied to several hyperspectral data sets, including some randomly rotated data, to elucidate the kinds of problems and the kinds of data for which SMT is well or poorly suited. The SMT is based on the product of K pairwise coordinate (Givens) rotations, and we also introduce and compare two novel approaches for estimating the most effective choice for K .
Keywords :
covariance matrices; geophysical image processing; graph theory; matched filters; signal detection; sparse matrices; Frobenius norm; K pairwise coordinate rotation; anomaly detection; covariance estimator; covariance matrix; dimension reduction; hyperspectral image processing; remote sensing; shrinkage-enhanced SMT; signal detection; sparse matrix transform; Approximation methods; Covariance matrix; Hyperspectral imaging; Pixel; Sparse matrices; Transforms; Anomalous change detection; anomaly detection; change detection; covariance matrix; hyperspectral imagery; matched filter; signal detection; sparse matrix transform (SMT);
Journal_Title :
Selected Topics in Signal Processing, IEEE Journal of
DOI :
10.1109/JSTSP.2010.2103924
