Kernel principal component and maximum autocorrelation factor analyses for change detection

Allan A. Nielsen; Morton J. Canty

doi:10.1117/12.829645

28 September 2009 Kernel principal component and maximum autocorrelation factor analyses for change detection

Allan A. Nielsen, Morton J. Canty

Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 74770T (2009) https://doi.org/10.1117/12.829645
Event: SPIE Remote Sensing, 2009, Berlin, Germany

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.

Citation Download Citation

Allan A. Nielsen and Morton J. Canty "Kernel principal component and maximum autocorrelation factor analyses for change detection", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770T (28 September 2009); https://doi.org/10.1117/12.829645

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