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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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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. |
format | Online Article Text |
id | pubmed-7482030 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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