ConvFormer-CD: Hybrid CNN–Transformer With Temporal Attention for Detecting Changes in Remote Sensing Imagery

Recently, the combination of Transformers and convolutional neural networks (CNNs) has witnessed significant advancements in change detection (CD) tasks. However, it remains unexplored how to interactively integrate long-range dependency and local information to enhance the model’s global-local context awareness for effectively mitigating pseudo-changes. In addition, accurate identification and distinction of building changes from complex backgrounds still pose challenges due to the insufficient semantic context modeling across time between bi-temporal images. To address these issues, we propose a hybrid model ConvFormer-CD with parallel convolution and multihead self-attention (MSA). This combination enables better interaction of global and local information, thereby enhancing the adaptability to complex scenarios. Moreover, we introduce a novel module called Temporal Attention to establish cross-temporal semantic relationships between image pairs, effectively highlighting change regions by learning shared and nonshared semantics. This enables our model to accurately detect changed targets even in scenarios characterized by intricate geo-spatial arrangements and distributions. To further refine the differences in bi-temporal images, we propose a difference integration module (DIM) that connects the encoder and the decoder to fuse high-level semantic features across channels. We conduct extensive experiments on four benchmark datasets, including LEVIR-CD, LEVIR-CD+, WHU-CD, and S2Looking-CD, which demonstrates that the proposed ConvFormer-CD outperforms other state-of-the-art (SOTA) methods. Our codes will be available at https://github.com/taomi-lab/ConvFormer-CD.