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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