Difference-Aware Multiscale Feature Aggregation Network for Building Change Detection

The application of remote sensing (RS) for building change detection (BCD) is indispensable for assessing shifts in land use and surface dynamics. Nevertheless, off-the-shelf deep learning-based BCD methods often suffer from incomplete change boundaries and pseudo changes, due to the insufficient utilization of difference information from bitemporal remote sensing images. To address these issues, we propose a difference-aware multiscale feature aggregation network (DMFANet) aimed at investigating more representative forms of change representation in BCD tasks. To facilitate comprehensive bitemporal feature alignment and differencing, the feature modulation module (FMM) is designed, which focuses on developing semantically robust and contextually enriched pyramidal feature representations by channel-spatial modulation. Subsequently, the cross-domain difference enhancement module (CDEM) is introduced to accurately locate changed building areas with intricate details by capturing multi-perspective difference information through the construction of different domain dependencies. Moreover, we propose a multi-scale context aggregation module (MCAM) to effectively adapt to building scale variations by aggregating multiscale difference features under the guidance of contextual information while mitigating the interference of redundant difference information. The empirical outcomes firmly confirm the superiority of our stream-lined network over nine cutting-edge approaches on the LEVIR-CD, WHU-CD, and SYSU-CD benchmarks, excelling both in terms of accuracy and efficiency. Code and pretrained models are accessible at https://github.com/SallyRonionGit/DMFANet.