Kernel principal component analysis for change detection

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationSPIE Europe Remote Sensing Conference
PublisherSPIE - International Society for Optical Engineering
Publication date2008
Publication statusPublished - 2008
EventSPIE Europe Remote Sensing Conference 2007 - Florence, Italy
Duration: 17 Sep 200720 Sep 2007

Conference

ConferenceSPIE Europe Remote Sensing Conference 2007
CountryItaly
CityFlorence
Period17/09/200720/09/2007

Cite this

Nielsen, A. A., & Morton, J. C. (2008). Kernel principal component analysis for change detection. In SPIE Europe Remote Sensing Conference SPIE - International Society for Optical Engineering.