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Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a nove...

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Detalles Bibliográficos
Autores principales: Li, Chenming, Wang, Yongchang, Zhang, Xiaoke, Gao, Hongmin, Yang, Yao, Wang, Jiawei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339065/
https://www.ncbi.nlm.nih.gov/pubmed/30626030
http://dx.doi.org/10.3390/s19010204
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author Li, Chenming
Wang, Yongchang
Zhang, Xiaoke
Gao, Hongmin
Yang, Yao
Wang, Jiawei
author_facet Li, Chenming
Wang, Yongchang
Zhang, Xiaoke
Gao, Hongmin
Yang, Yao
Wang, Jiawei
author_sort Li, Chenming
collection PubMed
description With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
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spelling pubmed-63390652019-01-23 Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data Li, Chenming Wang, Yongchang Zhang, Xiaoke Gao, Hongmin Yang, Yao Wang, Jiawei Sensors (Basel) Article With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches. MDPI 2019-01-08 /pmc/articles/PMC6339065/ /pubmed/30626030 http://dx.doi.org/10.3390/s19010204 Text en © 2019 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
Li, Chenming
Wang, Yongchang
Zhang, Xiaoke
Gao, Hongmin
Yang, Yao
Wang, Jiawei
Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title_full Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title_fullStr Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title_full_unstemmed Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title_short Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
title_sort deep belief network for spectral–spatial classification of hyperspectral remote sensor data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339065/
https://www.ncbi.nlm.nih.gov/pubmed/30626030
http://dx.doi.org/10.3390/s19010204
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