TY - JOUR
A2 - Hu, Xiaofei
AU - Gorelik, Nir
AU - Blumberg, Dan
AU - Rotman, Stanley R.
AU - Borghys, Dirk
PY - 2012
DA - 2012/07/31
TI - Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
SP - 628479
VL - 2012
AB - Accurate covariance matrix estimation for high-dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image whose number is greater than several times the number of bands. Estimating the covariance matrix with a number of pixels that is on the order of the number of bands or less will cause not only a bad estimation of the covariance matrix but also a singular covariance matrix which cannot be inverted. In this paper 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 quasilocal covariance matrix (QLRX) that uses the variance of the global covariance instead of the variances that are too small and cause a singular covariance. The second method is sparse matrix transform (SMT) 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.
SN - 2090-0147
UR - https://doi.org/10.1155/2012/628479
DO - 10.1155/2012/628479
JF - Journal of Electrical and Computer Engineering
PB - Hindawi Publishing Corporation
KW -
ER -