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Proceedings Paper

Kernel principal component and maximum autocorrelation factor analyses for change detection
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Paper 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.

Paper Details

Date Published: 28 September 2009
PDF: 6 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770T (28 September 2009); doi: 10.1117/12.829645
Show Author Affiliations
Allan A. Nielsen, Technical Univ. of Denmark (Denmark)
Morton J. Canty, Forschungszentrum Jülich GmbH (Germany)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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