SFCF-Net: A Cross-Hierarchical Progressive Feature Integration Network for UAV Image Change Detection

Remote sensing change detection (RSCD) is an important technique for extracting information about changes to the surface from bi-temporal remote sensing images. However, existing RSCD datasets are primarily based on satellite images, and those built from high-resolution UAV images are scarce. Therefore, this paper proposes a high-resolution, UAV-based change detection dataset (UAV-CD). In addition, UAV change detections differ significantly from satellite change detections. UAV images have higher spatial resolution and richer detailed information, but they are susceptible to interference from non-surface structural factors, such as shadows, illumination fluctuations, and vehicles. In contrast, satellite images provide wide coverage but lack sufficient detailed representation. Furthermore, most existing methods are specifically designed for satellite image characteristics, making them difficult to adapt to the high-detail and high-noise scenarios of UAV images. To overcome these challenges, we propose SFCF-Net. This introduces a Single-scale Global Feature Enhancement Module (SGFEM) to capture global contextual information from a single-scale perspective. The Multi-scale Dynamic Feature Integration Module (MDFIM) dynamically integrates semantic and structural information at different scales to effectively mitigate the problem of semantic inconsistency in single-scale features. The Multi-receptive Field Local Feature Fusion Module (MFLFM) aggregates local detail information through multi-scale convolutions to compensate for the lack of edge detail perception in global modelling. Experimental results on the UAV-CD, LEVIR-CD and CDD datasets demonstrate that SFCF-Net outperforms other baseline change detection networks, achieving detection accuracies of 92.73%, 92.95% and 90.84% respectively. The source code is available in:https://github.com/kwxt/SFCF-Net.git.