Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hyperspectral Image Segmentation based on Graph Processing over
  Multilayer Networks   Hyperspectral imaging is an important sensing technology with broad
applications and impact in areas including environmental science, weather, and
geo/space exploration. One important task of hyperspectral image (HSI)
processing is the extraction of spectral-spatial features. Leveraging on the
recent-developed graph signal processing over multilayer networks (M-GSP), this
work proposes several approaches to HSI segmentation based on M-GSP feature
extraction. To capture joint spectral-spatial information, we first customize a
tensor-based multilayer network (MLN) model for HSI, and define a MLN singular
space for feature extraction. We then develop an unsupervised HSI segmentation
method by utilizing MLN spectral clustering. Regrouping HSI pixels via
MLN-based clustering, we further propose a semi-supervised HSI classification
based on multi-resolution fusions of superpixels. Our experimental results
demonstrate the strength of M-GSP in HSI processing and spectral-spatial
information extraction.

27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg