Sampling Enhanced Contrastive Multi-View Remote Sensing Data Clustering With Long-Short Range Information Mining

作者：Renxiang Guan, Tianrui Liu, Wenxuan Tu, Chang Tang, Wenhan Luo, Xinwang Liu · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2025 · DOI：10.1109/tkde.2025.3580139 · 被引用次数：22 · 研究领域：Advanced Computing and Algorithms、Evaluation Methods in Various Fields、Remote Sensing and Land Use

Multi-view clustering (MVC) for remote sensing data has demonstrated significant potential in Earth observation, given its ability to aggregate multi-source information without relying on labels. Despite achieving compelling results through the combination of deep encoders and contrastive learning, existing algorithms still face two limitations: inadequate exploration of diverse spatial relationships and inability to guide the selection of sample pairs leads to blind sampling, both of which lead to suboptimal clustering performance. To tackle these challenges, we propose a sampling enhanced contrastive multi-view clustering method for remote sensing data, namely SEC-LSRM. The proposed method incorporates long- and short-range information mining to enhance clustering performance. By aggregating shortrange information extracted through autoencoders and longrange information obtained via graph autoencoders, our method improves the sampling quality of positive and negative sample pairs. To render the extracted features more compact, a multiview correlation reduction strategy is devised to filter out irrelevant information. With the extracted comprehensive features, an adaptive sampling strategy is designed to obtain high-quality positive and negative samples. Subsequently, we select positive and negative sample pairs based on these affinity matrices with idempotence and block diagonal constraints. Moreover, we integrate the optimization of these sample pairs and contrastive learn...