A faster way to compute the noise-adjusted principal components transform matrix

摘要

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.