Interaction in Transformer for Change Detection in VHR Remote Sensing Images

With the development of deep learning, very high-resolution remote sensing image change detection (VHRCD) methods are becoming more popular. However, the most existing change detection methods are not good at processing edge details and small target detection. To this end, in this paper, InterFormer, a bidirectional interactive framework based on transformer is proposed to find small changes and extract more accurate edge information of the change area. First, we designed an asymmetric Interaction Attention Module (IAM) to identify the edge details for the bi-temporal image. The IAM fully leverages the benefits of self-attention, performing feature fusion during feature extraction. This approach improves edge feature extraction capability and reduces the number of parameters, compared to other transformer methods. Second, we designed a global attention based feature fusion module called GFFM to enhance the detection performance of small targets. The GFFM further improves small target detection ability by augmenting the network's selectivity to spatial information during feature fusion. The method is applicable to scenarios involving small changes and possesses enhanced edge detection capabilities. Our method outperforms state-of-the-art counterparts on three public benchmarks and has fewer parameters.