Semi-Supervised SAR Image Change Detection via Structure-Optimized Complex-Valued Graph Contrastive Learning

Significant progress has been achieved by using the graph convolutional network (GCN) in image change detection. However, the limited quantity of labeled data and the inherent speckle noise adversely impact the generalization ability of the existing GCN-based methods in practical synthetic aperture radar (SAR) image applications. To address these challenges, we introduce the structure-optimized complex-valued graph contrastive learning network (SCGCLN) for semi-supervised SAR image change detection. Specifically, we explore how to learn effective feature representations from complex-valued SAR data with limited supervised information using the GCN architecture. We present a structure-optimized graph reconstruction strategy based on optimizing node features and edge structures. By combining efficient spectral clustering with graph reconnection, our method learns high-quality graph structures that enable the network to capture long-range dependencies, thereby mitigating the impact of speckle noise. Moreover, we construct a complex-valued graph contrastive learning (GCL) network to train a graph feature representation model from unlabeled SAR data. Subsequently, the pretrained model is fine-tuned for the downstream limited labeled SAR change detection task. The effectiveness of SCGCLN is validated through experimental results on three SAR image datasets.