Title :
A faster way to compute the noise-adjusted principal components transform matrix
Author_Institution :
Dept. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia
fDate :
11/1/1994 12:00:00 AM
Abstract :
The matrix for the noise-adjusted principal components (NAPC) transform is the solution of a generalized symmetric eigenvalue problem. Applied to remote sensing imagery, this entails the simultaneous diagonalization of data and noise covariance matrices. One of the two PC transforms of the original NAPC transform is replaced by several short, fast procedures. The total operation count for the computation of the NAPC transform matrix is halved
Keywords :
geophysical signal processing; geophysical techniques; image enhancement; infrared imaging; optical information processing; remote sensing; NAPC transform matrix; generalized symmetric eigenvalue problem; geophysical measurement technique; image enhancement; image processing; land surface optical imaging; multispectral method; noise covariance matrices; noise-adjusted; principal components transform matrix; remote sensing imagery; simultaneous diagonalization; visible infrared IR; Computational complexity; Covariance matrix; Eigenvalues and eigenfunctions; Image quality; Karhunen-Loeve transforms; Pattern classification; Pattern recognition; Remote sensing; Signal to noise ratio; Symmetric matrices;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
