A faster way to compute the noise-adjusted principal components transform matrix
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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. >
[1] Keinosuke Fukunaga,et al. Application of the Karhunen-Loève Expansion to Feature Selection and Ordering , 1970, IEEE Trans. Computers.
[2] Robert W. Newcomb,et al. On the simultaneous diagonalization of two semi-definite matrices , 1961 .
[3] P. Switzer,et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .
[4] J. B. Lee,et al. Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .