Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Spatial Correlation in Semi-Supervised Hyperspectral Unmixing
  under Polynomial Post-nonlinear Mixing Model   This paper presents a semi-supervised hyperspectral unmixing solution that
integrate the spatial information in the abundance estimation procedure. The
proposed method is applied on a nonlinear model based on polynomial
postnonlinear mixing model where characterizes each pixel reflections composed
of nonlinear function of pure spectral signatures added by noise. We
partitioned the image to classes where contains similar materials so share the
same abundance vector. The spatial correlation between pixels belonging to each
class is modelled by Markov Random Field. A Bayesian framework is proposed to
estimate the classes and corresponding abundance vectors alternatively. We
proposed sparse Dirichlet prior for abundance vector that made it possible to
use this algorithm in semi-supervised scenario where the exact involved
materials are unknown. In this approach, we just need to have a large library
of pure spectral signatures including the desired materials. An MCMC algorithm
is used to estimate the abundance vector based on generated samples. The result
of implementation on simulated data shows the prominence of proposed approach.

14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom