DFMamba: Difference-enhanced and adaptive frequency-aware Mamba for change detection

Recently, the Mamba architecture has demonstrated outstanding performance in natural language processing and general vision tasks, and has been rapidly extended to the field of Remote Sensing Change Detection (RSCD). However, existing Mamba-based change detection methods lack explicit modeling for precise difference features. The process of extracting difference features is prone to amplifying irrelevant noise, making it challenging to extract subtle changes and boundary information in complex remote sensing scenes. To address these challenges, we propose a novel network, DFMamba, integrating two innovative modules. First, the Difference Boost Mamba Fusion (DBMF) module employs differential contrast features to enhance the differences in the original features, thereby further amplifying the finer distinctions between the bi-temporal images. Second, the Adaptive Frequency-Aware (AFA) module generates adaptive frequency-domain signals using learnable filter coefficients, which suppress the irrelevant high-frequency noise introduced by the differential feature enhancement while enhancing information about changing boundaries. To evaluate model performance, experiments are conducted on five benchmark datasets (LEVIR-CD, LEVIR-CD+, WHU-CD, SYSU, and CDD). Results demonstrate that DFMamba achieves competitiveness comparable to multiple state-of-the-art models across all datasets, validating its effectiveness. The source code of our proposed model will be released at https://github.com/PriscaCommit/DFMamba.