Gradient-Guided Temporal Cross-Attention Transformer for High-Performance Remote Sensing Change Detection

Hao Zhang, Yang Wu, Huihui Song, Kaihua Zhang

Abstract: Given a group of bi-temporal remote sensing images acquired in the same geographical area, the task of change detection (CD) aims to detect and segment the change regions therein. Existing leading CD methods typically use the self-attention (SA) mechanism to directly fuse the concatenated features of the bi-temporal images. Despite demonstrated success, the SA focuses primarily on modeling spatial-wise relationships, rather than channel-wise relationships. Meanwhile, since the temporal direction is along the channel direction, this makes the SA difficult to model the temporal-wise relationship between the bi-temporal features, making it fail to learn the feature correspondence from the significantly changed regions. To this end, this letter presents a gradient-guided temporal cross-attention (Grad-TCA) mechanism for CD. First, we design a temporal cross-attention module (TCAM) that mixes the cross- and self-attention to model both temporal- and spatial-wise interactions, which fully mines the complementary cues between bi-temporal features to learn a strong feature presentation. Afterward, to further highlight the salient features between the change regions, we design a gradient-guided module (GGM) to enhance the difference of the learned bi-temporal features through feedback gradient information. Both the TCAM and the GGM construct our Grad-TCA module, which is seamlessly integrated into a transformer framework for end-to-end learning. Finally, to reduce the computation overhead, we design a simple change discrimination module (CDM) that outputs a score to directly filter out the unchanged features from the GGM with no need of passing the features through the decoder. Comprehensive evaluations on the two widely used benchmark datasets including LEVIR-CD and WHU-CD demonstrate that our model outperforms a variety of state-of-the-art methods.