FRCD: Feature Refine Change Detection Network for Remote Sensing Images

Change detection plays an important role in Earth surface analysis. Current change detection methods have achieved good performance in large flat areas, but change detection of detailed parts is still a great challenge, and the loss of detail causes many faults around the change boundaries and on small objects. By analyzing the feature map of the widely used U-Net architecture in existing methods, we ascribe the detail loss to the depletion of detailed features during the top-to-down delivery in the U-Net architecture. The Feature Refine Change Detection(FRCD) model is proposed in which the detection results are predicted directly from the multiscale features instead of the U-Net architecture. By direct prediction, the representation ability of details is enhanced, and thus the detection accuracy of boundaries and small objects improves. Moreover, the normal upsampling in direct prediction is replaced with the deformable upsampling, which delivers detailed information from the low-level to the high-level via the deformable convolution, allowing the results to further fit boundaries in the FRCD model. Experimental results on two datasets confirm the effectiveness of FRCD compared to state-of-the-art methods, and the change detection results of boundaries and small objects are improved significantly by the proposed method. Code will be available after the acceptance of the paper in https://github.com/ijnokml/cdfr.