Kernel one-class weighted sparse representation classification for change detection

In this letter, improved methods based on one-class sparse representation classifier (OCSRC) are proposed for change detection with multi-temporal multi-spectral remote sensing images. By adopting the weighted regularization and the kernel method, kernel one-class sparse representation classifier (K-OCSRC), one-class weighted sparse representation classifier (OCWSRC) and its kernel version, kernel one-class weighted sparse representation classifier (K-OCWSRC) are proposed. Performances of the OCSRC, K-OCSRC, OCWSRC, K-OCWSRC methods are tested with the flood dataset. Results show that the weighted methods (OCWSRC and K-OCWSRC) are less sensitive to the regularization parameter in the optimization process, and the kernel methods (K-OCSRC and K-OCWSRC) can distinctively improve change detection accuracies by solving the problem in the projected higher-dimensional space. Overall, the K-OCWSRC achieves the best change detection result as it can more accurately locate the flood affected areas while bringing in least undesirable false alarms.