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Hyperspectral Image Classification with Capsule Network Using Limited Training Samples
Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule net...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165568/ https://www.ncbi.nlm.nih.gov/pubmed/30231574 http://dx.doi.org/10.3390/s18093153 |
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author | Deng, Fei Pu, Shengliang Chen, Xuehong Shi, Yusheng Yuan, Ting Pu, Shengyan |
author_facet | Deng, Fei Pu, Shengliang Chen, Xuehong Shi, Yusheng Yuan, Ting Pu, Shengyan |
author_sort | Deng, Fei |
collection | PubMed |
description | Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs). |
format | Online Article Text |
id | pubmed-6165568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61655682018-10-10 Hyperspectral Image Classification with Capsule Network Using Limited Training Samples Deng, Fei Pu, Shengliang Chen, Xuehong Shi, Yusheng Yuan, Ting Pu, Shengyan Sensors (Basel) Article Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs). MDPI 2018-09-18 /pmc/articles/PMC6165568/ /pubmed/30231574 http://dx.doi.org/10.3390/s18093153 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Deng, Fei Pu, Shengliang Chen, Xuehong Shi, Yusheng Yuan, Ting Pu, Shengyan Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title | Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title_full | Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title_fullStr | Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title_full_unstemmed | Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title_short | Hyperspectral Image Classification with Capsule Network Using Limited Training Samples |
title_sort | hyperspectral image classification with capsule network using limited training samples |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165568/ https://www.ncbi.nlm.nih.gov/pubmed/30231574 http://dx.doi.org/10.3390/s18093153 |
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