Kernel principal component analysis for change detection

Kernel principal component analysis for change detection
Allan Aasbjerg Nielsen, Morton John Canty
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.
Type	Conference paper [With referee]
Conference	SPIE Europe Remote Sensing Conference
Editors	Lorenzo Bruzzone, Claudia Notarnicola, Francesco Posa
Year	2008 Month September Vol. 7109
Publisher	SPIE
Electronic version(s)	[pdf]
BibTeX data	[bibtex]
IMM Group(s)	Geoinformatics