Journal Article

Efficient Unsupervised Clustering of Hyperspectral Images via Flexible Multi-Anchor Graphs

Wang, Rong, Xin, Haonan, Li, Yihong, Wang, Ting, Cao, Zhe

2025

Overview

Unsupervised hyperspectral image (HSI) clustering is a fundamental yet challenging task due to high dimensionality and complex spectral–spatial characteristics. In this paper, we propose a novel and efficient clustering framework centered on adaptive and diverse anchor graph modeling. First, we introduce a parameter-free construction strategy that employs Entropy Rate Superpixel (ERS) segmentation to generate multiple anchor graphs of varying sizes from a single HSI, overcoming the limitation of fixed anchor quantities and enhancing structural expressiveness. Second, we propose an anchor-to-pixel label propagation mechanism to transfer anchor-level cluster labels back to the pixel level, reinforcing spatial coherence and spectral discriminability. Third, we perform clustering directly at the anchor level, which substantially reduces computational cost while retaining structure-aware accuracy. Extensive experiments on three benchmark datasets (Trento, Salinas, and Pavia Center) demonstrate the effectiveness and efficiency of our approach.

• Publisher: MDPI AG
• Subject: anchor graph modeling / Clustering / efficient clustering / ERS segmentation / Graphs / hyperspectral image clustering / Hyperspectral imaging / label propagation / Labels / Pixels / Semantics / unsupervised learning