Lighter and Robust: A Rotation-Invariant Transformer for VHR Image Change Detection

In recent years, change detection (CD) has emerged as an increasingly intricate research domain. However, in natural images, the orientation of objects is often aligned with the image boundaries, whereas in RS images, the imaging angles are random. As a result, existing CD methods encounter limitations when effectively representing vector features. In this article, we propose a rotation-invariant CD architecture named RFormer. It effectively utilizes direction-sensitive position embedding (DSPE) to represent features in RS images. To address the challenge of the quadratic growth in attention mechanism complexity with sequence length, we introduce low-cost cross attention (LC(2)A) to reduce its complexity to 1/C-2 . Furthermore, we employ the implicit timing extraction process (TEP) to represent interframe bitemporal features. TEP plays a crucial role in mitigating prediction biases caused by seasonal changes in land cover and prevents overconfident discrimination by the classifier in CD tasks. Experimental results demonstrate that RFormer achieves competitive performance on WHU, deeply supervised image fusion network (DSIFN)-CD, CDD, and LEVIR-CD datasets.