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The paper introduces CGD-CD Net, a novel self-supervised change detection method for remote sensing images that leverages a graph diffusion model and contrastive learning to address limitations of CNN-based approaches. The method constructs a graph from superpixel segmentation of bi-temporal images and employs a diffusion model to balance node states, aggregating higher-order feature information. Trained with a structure-sensitive contrastive loss, CGD-CD Net generates high-quality difference images for improved change detection, demonstrating superior performance on three datasets compared to existing self-supervised methods.
By integrating graph diffusion with contrastive learning, CGD-CD Net significantly reduces false positives in remote sensing change detection, outperforming existing self-supervised methods.
With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. Additionally, these methods fail to capture essential topological and structural information from remote sensing images, resulting in a high false positive rate. To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. Subsequently, a diffusion model is employed to balance the states of nodes within the graph, enabling the co-evolution of adjacency relationships and feature information, thereby aggregating higher-order feature information to obtain superior feature embeddings. The network is trained with a carefully crafted contrastive loss function to effectively capture high-level structural information. Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. The effectiveness and feasibility of the suggested method are confirmed by experimental results on three different datasets, which show that it performs better than several of the top SSL-CD methods.
