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Remote Sens. 2017, 9(4), 386; doi:10.3390/rs9040386 (registering DOI)

Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective

1
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2
Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
3
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 27 November 2016 / Revised: 13 April 2017 / Accepted: 16 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

Abstract

This paper presents a novel semi-supervised joint dictionary learning (S2JDL) algorithm for hyperspectral image classification. The algorithm jointly minimizes the reconstruction and classification error by optimizing a semi-supervised dictionary learning problem with a unified objective loss function. To this end, we construct a semi-supervised objective loss function which combines the reconstruction term from unlabeled samples and the reconstruction–discrimination term from labeled samples to leverage the unsupervised and supervised information. In addition, a soft-max loss is used to build the reconstruction–discrimination term. In the training phase, we randomly select the unlabeled samples and loop through the labeled samples to comprise the training pairs, and the first-order stochastic gradient descents are calculated to simultaneously update the dictionary and classifier by feeding the training pairs into the objective loss function. The experimental results with three popular hyperspectral datasets indicate that the proposed algorithm outperforms the other related methods. View Full-Text
Keywords: hyperspectral image classification; discriminative sparse representation; semi-supervised joint dictionary learning (S2JDL); soft-max loss hyperspectral image classification; discriminative sparse representation; semi-supervised joint dictionary learning (S2JDL); soft-max loss
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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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Xue, Z.; Du, P.; Su, H.; Zhou, S. Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective. Remote Sens. 2017, 9, 386.

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