Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial
  Diffusion Geometry   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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The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph