关键词: 遥感图像, 变化检测, 语义差距, 跳跃连接, Transformer, 交叉注意力

Abstract: Existing deep learning-based methods tend to focus on extracting advanced change semantic features, making it challenging to capture changes in ground object details, resulting in fuzzy boundaries and vulnerability to pseudo change. Meanwhile, the skip connection in the traditional U-shaped architecture is difficult for narrowing the semantic gap between the encoder and decoder. To solve the above problems, a multi-scale dual cross attention transformer network (MDCATNet) is proposed for remote sensing image change detection. In the encoder part, MDCATNet utilizes a primary feature conservation strategy and convolutional blocks with residual structures to construct the Siamese network with shared weights to extract multiscale features of the dual-temporal image. In the decoder part, in order to narrow the semantic gap between the encoder and the decoder, and to fully integrate the remote channel and spatial information of the multi-scale features, a novel multi-scale multi-head channel-spatial cross fusion Transformer module is proposed as an alternative to the traditional skip connection. In order to further refine the features and obtain more detailed change regions and smoother boundary contours, a channel cross attention refinement module is proposed for refining the features layer by layer from bottom to top and generating high-quality prediction maps. Experiments on LEVIR-CD and SYSU-CD datasets show that compared with the other six algorithms, MDCATNet achieves the best detection results in both quantitative evaluation and visualization, and has stronger generalization ability.

Key words: remote sensing image, change detection, semantic gap, skip connection, Transformer, cross attention