Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry

• URL: http://arxiv.org/abs/2103.15783v1
• Date: Mon, 29 Mar 2021 17:24:28 GMT
• Title: Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry
• Authors: Sam L. Polk and James M. Murphy
• Abstract summary: 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. We show that incorporating spatial regularization into a multiscale clustering framework corresponds to smoother and more coherent clusters when applied to HSI data.
• Score: 9.619814126465206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (HSI). The M-SRDL clustering algorithm extracts clusterings at many scales from an HSI 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 corresponds to smoother and more coherent clusters when applied to HSI data and leads to more accurate clustering labels.

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