CSANet: Cross-Temporal Interaction Symmetric Attention Network for Hyperspectral Image Change Detection

Abstract

Deep learning methods have been extensively applied to hyperspectral (HS) image change detection task and achieved promising performance. However, the beneficial joint spatial-spectral-temporal information provided by the HS images has not been fully used. Since the bi-temporal HS images are highly symmetric, in this letter, we propose a novel Cross-Temporal Interaction Symmetric Attention Network (CSANet), which can effectively extract and integrate the joint spatial-spectral-temporal features of the HS images, at the same time to enhance feature discrimination ability of the changes. Specifically, we propose a novel Cross-Temporal Interaction Symmetric Attention (CSA) module to interact the bi-temporal HS information, where self attentions are combined to enhance the feature representation ability of each temporal image, and the cross-temporal attention is utilized to integrate the difference features oriented from each temporal feature embeding. On this basis, we design a siamese network structure equipped with the CSA to hierarchically extract the change information in a symmetric pattern. Experimental results on three public HS image change detection datasets show that the proposed CSANet change detection framework achieves a significant improvement when comparing with the state-of-the-art methods. The source code of the proposed framework will be released at https://github.com/srxlnnu.