Graph-Based Multi-Resolution Cosegmentation for Coarse-to-Fine Object-Level SAR Image Change Detection

The ongoing launch of high-resolution satellites has led to a significant increase in the volume of synthetic aperture radar data, resulting in a high-resolution and high-revisit Earth observation that efficiently supports subsequent high-resolution SAR change detection. To address the issues of speckle noise interference, insufficient integrity of change targets and blurred boundary location of high-resolution SAR change detection, we propose a coarse-to-fine framework based on the multi-scale segmentation and hybrid structure graph (HSG), which consists of three modules: multi-scale segmentation, difference measurement, and change refinement. First, we propose a graph-based multi-resolution co-segmentation (GMRCS) in the multi-scale segmentation module to generate hierarchically nested superpixel masks. And, a two-stage ranking (TSR) strategy is designed to help GMRCS better approximate the target edges and preserve the spatio-temporal structure of changed regions. Then, we introduce a graph model and measuring difference level based on the HSG. The multi-scale difference image (DI) is generated by constructing the HSG for bi-temporal SAR images and comparing the consistency of the HSGs to reduce the effect of speckle noise. Finally, the coarse-scale change information is gradually mapped to the fine-scale based on the multi-scale fusion refinement (FR) strategy, and we can get the binary change map (BCM). Experimental results on three high-resolution SAR change detection datasets demonstrates the superiority of our proposed algorithm in preserving the integrity and structural precision of change targets compared with several state-of-the-art methods.