MSFNet: Multiscale Spatial-Frequency Feature Fusion Network for Remote Sensing Change Detection

Deep learning models utilizing spatial features have demonstrated outstanding performance in remote sensing change detection. However, current deep learning methods relying on spatial features still exhibit limitations in accurately detecting changed edge regions and insignificant areas. To overcome this limitation, we propose the multiscale spatial-frequency feature fusion network (MSFNet) for change detection. The network aims to enhance the detection capability of change edge and insignificant regions. In the encoding stage, we introduce a frequency difference feature extraction module to enhance the frequency difference features by extracting high-frequency information that represents changes and low-frequency information that represents invariance at each feature scale. This approach effectively mitigates the issue of incomplete change information caused by relying solely on a single spatial feature dimension for feature extraction. In the decoding stage, a spatial frequency difference fusion module is introduced to map the extracted frequency difference information back to the spatial domain. This module enhances the network's semantic modeling capability for change regions while mitigating the degradation of edge detection accuracy resulting from information loss during feature extraction. The experimental results demonstrate that MSFNet has remarkable performance on both the LEVIR-CD and WHU-CD datasets, with an F1 score of 91.66 and an IoU of 84.41 on the LEVIR-CD dataset, and an F1 score of 90.87 and an IoU of 82.80 on the WHU-CD dataset.