IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

Qing Guo, Ruofei Wang, Rui Huang, Renjie Wan, Shuifa Sun, Yuxiang Zhang

Abstract: Change detection (CD) is a crucial task in various real-world applications, aiming to identify regions of change between two images captured at different times. However, existing approaches mainly focus on designing advanced network architectures that map feature differences to change maps, overlooking the impact of feature difference quality. In this paper, we approach CD from a different perspective by exploring how to optimize feature differences to effectively highlight changes and suppress background regions. To achieve this, we propose a novel module called the iterative difference-enhanced transformers (IDET). IDET consists of three transformers: two for extracting long-range information from the bi-temporal images, and one for enhancing the feature difference. Unlike previous transformers, the third transformer utilizes the outputs of the first two transformers to guide iterative and dynamic enhancement of the feature difference. To further enhance refinement, we introduce the multi-scale IDET-based change detection approach, which utilizes multi-scale representations of the images to refine the feature difference at multiple scales. Additionally, we propose a coarse-to-fine fusion strategy to combine all refinements. Our final CD method surpasses nine state-of-the-art methods on six large-scale datasets across different application scenarios. This highlights the significance of feature difference enhancement and demonstrates the effectiveness of IDET. Furthermore, we demonstrate that our IDET can be seamlessly integrated into other existing CD methods, resulting in a substantial improvement in detection accuracy.