ENHANCED CHANGE DETECTION USING NONLINEAR FEATURE EXTRACTION

This paper presents an application of the kernel principal component analysis aiming at spectrally aligning optical images before the application of change detection techniques. The approach relies on the extraction of nonlinear features from a selected subset of pixels representing unchanged areas in the bi-temporal images. Both images are then projected into the new space defined by the eigenvectors associated to largest variance (eigenvalues). In the transformed space, unchanged pixels are mapped next to each other, thus reducing within-class variance. The difference image that results from subtracting the projected datasets is likely to provide a more suitable representation for detecting changes. A subset of two Landsat TM scenes validates the proposed approach. The new representation is studied thanks to the change vector analysis and to the support vector domain description.