PDTE: Pseudo Dual-View Contrastive Learning via Taylor Expansion Optimization for Remote Sensing Image Clustering

Image clustering is a crucial task in remote sensing data processing. Nevertheless, relying solely on traditional machine learning algorithms is insufficient for processing large amounts of images and accurately clustering them. Some recent works have successfully applied deep learning to remote sensing image clustering, significantly improving clustering performance. However, current deep clustering methods struggle to correctly assign boundary samples that are close to multiple clusters, as their features are very similar to samples in different clusters. To this end, we propose a method called pseudo dual-view contrastive learning via Taylor expansion optimization for remote sensing image clustering, namely, PDTE. Specifically, we introduce the pseudo dual-view contrastive learning framework with random aggressive augmentation to explore the potential information and subtle feature differences of images, and optimize its objective function using Taylor expansion to enlarge the difference in distance between boundary samples and each cluster. This allows us to assign boundary samples accurately and improves the clustering performance. Extensive experiments conducted on five datasets show that PDTE has superior clustering performance.