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arXiv · 2103.15783

Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry

Sam L. Polk·James M. Murphy
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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.

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Sam L. Polk, James M. Murphy. 2022-04-07. Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry. https://arxiv.org/abs/2103.15783

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