Abstract:

In order to solve the polarimetric synthetic aperture radar (PolSAR) image change detection problem, a PolSAR image change detection method is proposed combining the region information with deep convolutional neural network (DCNN). The superpixel segmentation algorithm and the superpixel combination algorithm are utilized for extracting region information, then a difference image is obtained using region information and Wishart likelihood ratio. Second, a preclassification algorithm is used to obtain the pseudotraining samples and the samples which are ready to be classified. Third, the DCNN is trained using the pseudotraining samples. Finally, the trained DCNN is used to classify the samples that are to be classified to get the final results. Experimental results show that, compared with several existing PolSAR change detection methods, the proposed method can get better results.