Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry

TL;DR

This paper introduces M-SRDL, a multiscale clustering algorithm for hyperspectral images that leverages spectral-spatial diffusion geometry to produce coherent, accurate clusters across multiple scales.

Contribution

The paper presents a novel multiscale clustering method that integrates spatial regularization with diffusion distances for hyperspectral image analysis.

Findings

Produces smoother, more coherent clusters

Achieves more accurate clustering labels

Extracts multiscale clusterings effectively

Abstract

Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework results in smoother and more coherent clusters when applied to hyperspectral data, yielding more accurate clustering labels.