Kernel principal component analysis for change detection

Allan A. Nielsen; Morton J. Canty

doi:10.1117/12.800141

10 October 2008 Kernel principal component analysis for change detection

Allan A. Nielsen, Morton J. Canty

Proceedings Volume 7109, Image and Signal Processing for Remote Sensing XIV; 71090T (2008) https://doi.org/10.1117/12.800141
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially.

Citation Download Citation

Allan A. Nielsen and Morton J. Canty "Kernel principal component analysis for change detection", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090T (10 October 2008); https://doi.org/10.1117/12.800141

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