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Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network
The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516202/ https://www.ncbi.nlm.nih.gov/pubmed/34648508 http://dx.doi.org/10.1371/journal.pone.0254542 |
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author | Wang, Zhengyang Tian, Shufang |
author_facet | Wang, Zhengyang Tian, Shufang |
author_sort | Wang, Zhengyang |
collection | PubMed |
description | The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification. |
format | Online Article Text |
id | pubmed-8516202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85162022021-10-15 Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network Wang, Zhengyang Tian, Shufang PLoS One Research Article The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification. Public Library of Science 2021-10-14 /pmc/articles/PMC8516202/ /pubmed/34648508 http://dx.doi.org/10.1371/journal.pone.0254542 Text en © 2021 Wang, Tian https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wang, Zhengyang Tian, Shufang Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title | Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title_full | Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title_fullStr | Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title_full_unstemmed | Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title_short | Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network |
title_sort | lithological information extraction and classification in hyperspectral remote sensing data using backpropagation neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8516202/ https://www.ncbi.nlm.nih.gov/pubmed/34648508 http://dx.doi.org/10.1371/journal.pone.0254542 |
work_keys_str_mv | AT wangzhengyang lithologicalinformationextractionandclassificationinhyperspectralremotesensingdatausingbackpropagationneuralnetwork AT tianshufang lithologicalinformationextractionandclassificationinhyperspectralremotesensingdatausingbackpropagationneuralnetwork |