CTIDRNet: Cross-Temporal Interaction With Difference Refinement Network for Remote Sensing Image Change Detection

Remote sensing change detection (RSCD) has achieved creditable success in recent years. However, the challenge of identifying changed objects with shape details persists in RSCD. In this letter, we proposed a cross-temporal interaction with difference refinement network (CTIDRNet) to solve interference-caused fake change and incomplete irregular change shape in RSCD tasks. Specifically, by combining cross-attention and self-attention to steer the temporal feature interaction of each input, we design a temporal feature attention (TFA) module to excavate the potential relation of change areas and suppress the unchanged object interference. Afterward, a deformable convolution is used to design a difference feature refinement (DFR) architecture to capture temporal difference information at diverse feature levels. At last, we proposed a multiscale-guided fusion (MGF) module to fuse pyramid features, thereby dealing with scaling changes. Experimental results on three datasets show that CTIDRNet can extract irregularly changed areas effectively, and the evaluation result outperforms other SOTA methods, with an improvement of 1.79%–19.82%, 2.9%–11.07%, and 0.97%–8.91% in terms of F1 for CDD, SYSU, and LEVIR datasets, respectively. The demo code of this work is publicly available at https://github.com/lucyjiong/CTIDR .