Leveraging structural information for enhanced coherent change detection

We consider the two-pass coherent change detection problem for SAR imaging. Inspired by classical maximum likelihood-based coherent change detectors (Jakowatz, 1996) and multi-polarization SAR change detection techniques (Novak, 2005), we propose a method of incorporating underlying structural image information using specially formulated kernels. In particular, we utilize a class of convolutional edge detection kernels to extract underlying edge information in the scene of interest given noisy and potentially incomplete data. We then adapt existing multi-polarization SAR change detection methods to incorporate such edge information to improve the quality and robustness of resulting change maps. We validate the proposed method using real-world SAR images from the CCD Challenge Problem dataset and demonstrate improved change detection performance using empirical ROC studies.