DCCANet: Dual-Domain With Collaborative Cross-Mapping Attention Network for Remote Sensing Change Detection

Remote Sensing Change Detection (RSCD) is the process of identifying and extracting change regions by analyzing and comparing bitemporal remote sensing images of the same geographical region. However, effectively suppressing the interference of environmental factors and noise while achieving efficient global context modeling remains a critical scientific challenge. To address this issue, this paper proposes a Dual-Domain with Collaborative Cross-Mapping Network (DCCANet), which collaboratively utilizes frequency and spatial domain information for joint modeling. Specifically, the Frequency Enhancement Module (FEM) leverages the natural spectral separation properties of the Discrete Cosine Transform (DCT) to decouple highly entangled spatial features and utilizes a Top-$n$frequency selection strategy, thereby effectively mitigating the interference of environmental factors and noise on detection accuracy. The Difference Detection Module (DDM) utilizes a difference-guided spatial attention mechanism to effectively suppress noise interference, achieving more efficient difference feature extraction. Furthermore, the Global Information Interaction Module (GIIM) employs a novel cross-pooling mechanism to achieve better global context modeling in complex scenes while significantly reducing computational costs. Experimental results on three challenging datasets (LEVIR-CD, WHU-CD, and GZ-CD) show that DCCANet achieves F1 scores of 91.71%, 93.75%, and 87.10%, respectively, demonstrating its superior performance compared to state-of-the-art methods. The code will be available athttps://github.com/djy5/DCCANet.