A hybrid deep fuzzy clustering framework with fusion-based learning for robust SAR change detection

Multi-temporal Synthetic Aperture Radar (SAR) images are essential in detecting changes in the environment, analyzing urban growth, and assessing disasters. Nonetheless, it is difficult to reliably detect meaningful changes because of speckle noise, radiometric variations, and inhomogeneous terrain features that severely impair the accuracy of the detection. This paper presents a hybrid change detection framework that integrates Fusion-based Difference Imaging (FDI) with a deep feature-guided clustering method. The proposed FDI module enhances sensitivity to subtle backscatter variations, which combines complementary difference representations to enhance the discriminability of changed and unchanged regions. These representations are refined through a deep clustering process that improves separability and reduces false alarms. Extensive experiments on benchmark multi-temporal SAR data sets indicate that the proposed approach has high detection accuracy and robustness with overall accuracy (PCC) of up to 99.97%, a Kappa coefficient of up to 98.30% and an F1-score of up to 99.59% across various datasets. The proposed approach consistently outperforms competing methods across evaluated datasets and different speckle conditions. The results show that fusion-based representation learning, with uncertainty-aware clustering, is a scalable, efficient, and flexible solution to robust SAR change detection in challenging imaging conditions.