Sparse principal component analysis in hyperspectral change detection

Allan Aasbjerg Nielsen, Rasmus Larsen, Jacob Schack Vestergaard

AbstractThis contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very similar although the loadings are very different from their usual non-sparse counterparts. The choice of three wavelength regions as being most important for change detection demonstrates the feature selection capability of sparse PCA.
KeywordsAirborne remote sensing, HyMap, feature selection
TypeConference paper [With referee]
ConferenceSPIE Europe Remote Sensing Conference 8180
Year2011    Month September
AddressPrague, Czech Republic
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics, Geoinformatics