Kernel principal component and maximum autocorrelation factor analyses for change detection [7477-28]

Abstract

Kernel versions of the principal components (PCA) and maximum autocorrelation factor (MAF) transformations are used to postprocess change images obtained with the iteratively re-weighted multivariate alteration detection (MAD) algorithm. It is found that substantial improvements in the ratio of signal (change) to background noise (no change) can be obtained especially with kernel MAF.

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Cite this paper

@inproceedings{Nielsena2009KernelPC, title={Kernel principal component and maximum autocorrelation factor analyses for change detection [7477-28]}, author={Allan A. Nielsena and Morton J. Cantyb and Lorenzo Bruzzone and Claudia Notarnicola and Francesco Posa}, year={2009} }