Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm

TL;DR

This paper introduces a novel unsupervised hyperspectral image classification method using nonlocal total variation and a primal-dual algorithm, outperforming standard clustering techniques on synthetic and real data.

Contribution

It presents a new graph-based nonlocal total variation approach combined with a primal-dual algorithm for improved unsupervised hyperspectral image classification.

Findings

Outperforms spherical K-means, NMF, and MBO schemes.

Effective on both synthetic and real-world hyperspectral data.

Provides a stable and efficient clustering method.

Abstract

In this paper, a graph-based nonlocal total variation method (NLTV) is proposed for unsupervised classification of hyperspectral images (HSI). The variational problem is solved by the primal-dual hybrid gradient (PDHG) algorithm. By squaring the labeling function and using a stable simplex clustering routine, an unsupervised clustering method with random initialization can be implemented. The effectiveness of this proposed algorithm is illustrated on both synthetic and real-world HSI, and numerical results show that the proposed algorithm outperforms other standard unsupervised clustering methods such as spherical K-means, nonnegative matrix factorization (NMF), and the graph-based Merriman-Bence-Osher (MBO) scheme.