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...

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Autores principales:	Zhengyang Wang, Shufang Tian
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ecf15f30007a4ffca740a10980599bef
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spelling	oai:doaj.org-article:ecf15f30007a4ffca740a10980599bef2021-12-02T20:13:41ZLithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network.1932-620310.1371/journal.pone.0254542https://doaj.org/article/ecf15f30007a4ffca740a10980599bef2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254542https://doaj.org/toc/1932-6203The 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.Zhengyang WangShufang TianPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0254542 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhengyang Wang Shufang Tian Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network.
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	article
author	Zhengyang Wang Shufang Tian
author_facet	Zhengyang Wang Shufang Tian
author_sort	Zhengyang Wang
title	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_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_sort	lithological information extraction and classification in hyperspectral remote sensing data using backpropagation neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ecf15f30007a4ffca740a10980599bef
work_keys_str_mv	AT zhengyangwang lithologicalinformationextractionandclassificationinhyperspectralremotesensingdatausingbackpropagationneuralnetwork AT shufangtian lithologicalinformationextractionandclassificationinhyperspectralremotesensingdatausingbackpropagationneuralnetwork
_version_	1718374800109338624

Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network.

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