Scribble-Guided Structural Regression Fusion for Multimodal Remote Sensing Change Detection

Accurate change detection (CD) in multitemporal multimodal remote sensing images is crucial for numerous applications. However, existing unsupervised CD methods often face challenges in suppressing background noise, preserving fine-grained boundaries, and maintaining spatial coherence of target regions. To overcome these limitations, this study proposes a novel Scribble-Guided Structural Regression Fusion (SG-SRF) framework, which integrates sparse scribble annotations as lightweight priors into a dynamic regression mechanism. Specifically, the framework employs a scribble distance map to refine hypergraph Laplacian matrices, thereby optimizing feature representation for critical targets while suppressing irrelevant backgrounds. Experimental results demonstrate that the proposed method significantly outperforms traditional unsupervised methods in detecting complete and accurate change objects with minimal scribble input. Notably, the scribble guidance offers an efficient and cost-effective solution to the inherent limitations of unsupervised approaches, enabling more precise change detection without extensive labeled datasets. This work aims to bridge the gap between unsupervised adaptability and supervised accuracy, offering significant potential for practical CD applications. The source code will be made publicly available at https://github.com/MissYongjie/SG-SRF.