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Keywords = semi-supervised joint dictionary learning (S2JDL)

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29 pages, 6188 KiB  
Article
Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective
by Zhaohui Xue, Peijun Du, Hongjun Su and Shaoguang Zhou
Remote Sens. 2017, 9(4), 386; https://doi.org/10.3390/rs9040386 - 19 Apr 2017
Cited by 17 | Viewed by 5552
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 [...] Read more.
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. Full article
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