StyleFormer: Spatial–Temporal Style Projecting Bidirectional Interactive Transformer for Change Detection

Remote sensing image change detection is an important means for Earth monitoring task, which has a wide application prospect. In multitemporal optical remote sensing, there are inherent differences in factors such as lighting and sensors. This leads to the coupling of content change and image style change, making it difficult to distinguish. Therefore, a meaningful thinking for change detection is to decouple and capture the real changes of ground objects from multitemporal images. Based on this motivation, a novel general change detection architecture is explored, StyleFormer. It first proposes the concept of spatial–temporal style base and no longer constrains to semantic representation in a single image style. Instead, it introduces a spatial–temporal interactive style projection layer between bitemporal images, which projects the unseen diverse styles into the consistent expression space for change detection. Furthermore, an iterative interaction strategy of Transformer and CNN features is proposed to mine spatial–temporal context information more finely. It solves the lack of local perception and nonhierarchical features in ViT, and improves the model expression ability. After that, a change prior-guided cross-attention is introduced to fuse bitemporal features. It can adaptively enhance the change feature and improve the perception ability for small changes in remote sensing scenes. Sufficient experiments on four typical change detection datasets show that the proposed method is superior to the state-of-the-art methods. Especially on the datasets CDD-CD and SYSU-CD, the F1 score improved to 96.08% and 83.29%. The code of this work will be available at https://github.com/Tom-Dongfang/change-detection-StyleFormer.