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Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selecte...

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Autores principales: Li, Yanyi, Wang, Jian, Gao, Tong, Sun, Qiwen, Zhang, Liguo, Tang, Mingxiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482030/
https://www.ncbi.nlm.nih.gov/pubmed/32952545
http://dx.doi.org/10.1155/2020/8886932
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author Li, Yanyi
Wang, Jian
Gao, Tong
Sun, Qiwen
Zhang, Liguo
Tang, Mingxiu
author_facet Li, Yanyi
Wang, Jian
Gao, Tong
Sun, Qiwen
Zhang, Liguo
Tang, Mingxiu
author_sort Li, Yanyi
collection PubMed
description To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.
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spelling pubmed-74820302020-09-18 Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images Li, Yanyi Wang, Jian Gao, Tong Sun, Qiwen Zhang, Liguo Tang, Mingxiu Comput Intell Neurosci Research Article To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images. Hindawi 2020-09-01 /pmc/articles/PMC7482030/ /pubmed/32952545 http://dx.doi.org/10.1155/2020/8886932 Text en Copyright © 2020 Yanyi Li et al. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Li, Yanyi
Wang, Jian
Gao, Tong
Sun, Qiwen
Zhang, Liguo
Tang, Mingxiu
Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title_full Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title_fullStr Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title_full_unstemmed Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title_short Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images
title_sort adoption of machine learning in intelligent terrain classification of hyperspectral remote sensing images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482030/
https://www.ncbi.nlm.nih.gov/pubmed/32952545
http://dx.doi.org/10.1155/2020/8886932
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