Kernel entropy component analysis in remote sensing data clustering
This paper proposes the kernel entropy component analysis (KECA) for clustering remote sensing data. The method generates nonlinear features that reveal structure related to the Renyi entropy of the input space data set. Unlike other kernel feature extraction methods, the top eigenvalues and eigenvectors of the kernel matrix are not necessarily chosen. Data are interestingly mapped with a distinct angular structure, which is exploited to derive a new angle-based spectral clustering algorithm based on the mapped data. An out-of-sample extension of the method is also presented to deal with test data. We focus on cloud screening from MERIS images. Several images are considered to account for the high variability of the problem. Good results show the suitability of the proposal.