Deep Clustering for Hyperspectral Images: A Comprehensive Survey From Single-View to Multiview Paradigms

Abstract

Hyperspectral image (HSI) contains rich spectral information as well as spatial structural information, providing an important data source for a wide range of remote sensing applications. However, its high dimensionality, band redundancy, and the high cost of annotation limit the practical application of traditional methods that rely on supervised information. As a typical unsupervised learning task, clustering has received extensive attention. In recent years, with the rapid development of deep learning, deep clustering has significantly improved the unsupervised feature modeling capability for HSI by combining deep representation learning with clustering analysis. Moreover, it has gradually evolved from single-view modeling to multiview collaborative modeling, becoming an important research trend in this field. This article presents a systematic survey of deep clustering for HSI. It first introduces the basic concepts of HSI, deep clustering, and multiview clustering. Then, following the technical evolution from single-view to multiview paradigms, existing methods are systematically reviewed and categorized. Next, mainstream public datasets, commonly used evaluation metrics, and experimental comparisons of representative methods are summarized. Finally, this survey discusses the main challenges in current research, including cross-scene generalization, computational efficiency, multiview fusion effectiveness, robustness to uncertainty, as well as semantic interpretability, and further outlines potential directions for future research.