Change Detection in SAR Images Based on Progressive Nonlocal Theory

For multitemporal synthetic aperture radar (SAR) images, the change detection methods based on nonlocal theory can well suppress the adverse effects of coherent speckle noise over the change detection results. However, effectively retaining the edge information of the changed area is still a challenging task. To overcome this problem, this study proposes a change detection method based on progressive nonlocal theory. First, the progressive nonlocal theory is used to extract the spatial–temporal nonlocal information from multitemporal SAR images. Compared with the traditional nonlocal theory, the progressive nonlocal theory proposed in this study has three distinctive characteristics: 1) the progressive nonlocal neighborhood from the matching window to the search window; 2) the progressive optimization of matching window weight from the isotropic Gaussian distribution to the irregular distribution; and 3) the progressive increase of noise level from the 2 sigma principle to the 4/3 sigma principle (the noise level corresponding to the 4/3 sigma principle is 1.5 times the noise level corresponding to the 2 sigma principle). The difference image is then obtained by using the spatial–temporal nonlocal information and the ratio operator. Finally, the change map is obtained by applying a threshold segmentation method to the difference image. Two data sets were used for the testing, and it was shown that compared with other advanced methods, the method proposed in this study can better retain the edge information of the changed area and improve the Kappa coefficient and F1 score of the change map.