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Remote Sens. 2017, 9(10), 1017; https://doi.org/10.3390/rs9101017

Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images

1,†
,
2,†
,
2
,
3
and
1,*
1
College of Computer Science and Technology, Anhui University, Hefei 230601, China
2
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WuhanUniversity, Wuhan 430079, China
These authors contributed equally to the paper as first authors.
*
Author to whom correspondence should be addressed.
Received: 28 August 2017 / Revised: 23 September 2017 / Accepted: 28 September 2017 / Published: 30 September 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Hyperspectral image (HSI) clustering has drawn increasing attention due to its challenging work with respect to the curse of dimensionality. In this paper, we propose a novel class probability propagation of supervised information based on sparse subspace clustering (CPPSSC) algorithm for HSI clustering. Firstly, we estimate the class probability of unlabeled samples by way of partial known supervised information, which can be addressed by sparse representation-based classification (SRC). Then, we incorporate the class probability into the traditional sparse subspace clustering (SSC) model to obtain a more accurate sparse representation coefficient matrix accompanied by obvious block diagonalization, which will be used to build the similarity matrix. Finally, the cluster results can be obtained by applying the spectral clustering on similarity matrix. Extensive experiments on a variety of challenging data sets illustrate that our proposed method is effective. View Full-Text
Keywords: hyperspectral images; class probability; supervised information; sparse subspace clustering hyperspectral images; class probability; supervised information; sparse subspace clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yan, Q.; Ding, Y.; Xia, Y.; Chong, Y.; Zheng, C. Class Probability Propagation of Supervised Information Based on Sparse Subspace Clustering for Hyperspectral Images. Remote Sens. 2017, 9, 1017.

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