GRSG-DAF: A Global Robust Structured Graph Approach With Difference-Aware Filtering for SAR Image Change Detection

Synthetic aperture radar (SAR) image change detection plays a crucial role in monitoring environmental changes and landform evolution. However, existing methods often struggle to retain details while reducing computational cost, or lack the ability to effectively incorporate global information, especially under complex and noisy conditions. To address these challenges, we propose a novel global robust structured graph approach with difference-aware filtering (GRSG-DAF), which effectively enhances the detection of changed areas by combining pixel-level information with the advantages of the superpixel-based structured graph. First, we construct a structured graph by integrating difference information, utilizing superpixel co-segmentation and adaptive affinity weighting, thus significantly reducing computational complexity and preserving critical structural patterns. Second, an image reconstruction process is implemented, utilizing a feature propagation mechanism to improve the contextual representation of the image. Finally, a difference-aware guided filtering (DAGF) process is developed by integrating the inherent guidance from the original pixel-level image, preserving boundary structures and spatial details. The experimental results demonstrate that our proposed method outperforms state-of-the-art methods on five benchmark datasets, achieving higher accuracy while balancing effectiveness and efficiency in change detection.