SIAMESE RECURRENT RESIDUAL REFINEMENT NETWORK FOR HIGH-RESOLUTION IMAGE CHANGE DETECTION

In this study, we propose a siamese recurrent residual refinement network (SR(3)Net) for change detection in high-resolution remote sensing images. SR(3)Net uses the siamese network to fully extract multi-scale features of bi-temporal images. The obtained multi-scale feature maps are input to the multi-level difference module (MDM) to generate difference feature maps. The residual refinement module (RRM) with residual refinement blocks (RRBs) learns the residual between the intermediate change map and the ground truth by alternately exploiting low-level and high-level integrated difference features. Moreover, RRBs can obtain complementary information of the intermediate predictions and add residuals to the intermediate prediction to refine the change map. Experiments on the WHU-CD dataset show that the proposed method outperforms state-of-the-art methods.