Frequency-Domain Feature Interaction Combined With Multiscale Attention for Remote Sensing Change Detection

Change detection in remote sensing images has seen significant advancement due to the powerful discriminative capabilities of deep convolutional networks. However, the domain gap and pseudo-changes between the bi-temporal images, caused by variations in imaging conditions such as illumination, shadow, and background, remain a challenge. Furthermore, multi-scale variations in complex scenes complicate the accurate identification of change regions and their boundary delineation. To address these issues, this paper introduces the frequency domain feature interaction and multi-scale attention mechanism network (FIMANet). Specifically, to mitigate the impact of pseudo-change interference, the FIMANet reduces the domain gap and facilitates information coupling within intralevel representations through frequency domain feature interaction (FDFI). To prevent information loss and noise introduction, a multiple kernel inception (MKI) module is devised to capture multi-scale features and perform progressive fusion. Finally, to enhance the extraction of changes in scale-sensitive regions, the FIMANet constructs a cross-scale feature aggregator (CSFA) module, composed of attention at various scales and a transformer, to capture fine-grained details and global dependencies. Comparative experiments with nine methods on three commonly used datasets validate the effectiveness of FIMANet, achieving the highest F1 score of 73.98% on the CLCD dataset, 90.55% on the WHU-CD, and 91.01% on the LEVIR-CD. Code is available at https://github.com/zxXie-Air/FIMANet.