Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In o...

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Autores principales:	Wang Ping, Jie Fu, Wenyu Qiao, Muhammad Yasir, Sheng Hui, Md Sakaouth Hossain, Shah Nazir
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/cb040348bec444b597a9899c3095a96c
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spelling	oai:doaj.org-article:cb040348bec444b597a9899c3095a96c2021-11-08T02:37:02ZDecision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China1875-919X10.1155/2021/1376167https://doaj.org/article/cb040348bec444b597a9899c3095a96c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1376167https://doaj.org/toc/1875-919XThere are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.Wang PingJie FuWenyu QiaoMuhammad YasirSheng HuiMd Sakaouth HossainShah NazirHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Wang Ping Jie Fu Wenyu Qiao Muhammad Yasir Sheng Hui Md Sakaouth Hossain Shah Nazir Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
description	There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.
format	article
author	Wang Ping Jie Fu Wenyu Qiao Muhammad Yasir Sheng Hui Md Sakaouth Hossain Shah Nazir
author_facet	Wang Ping Jie Fu Wenyu Qiao Muhammad Yasir Sheng Hui Md Sakaouth Hossain Shah Nazir
author_sort	Wang Ping
title	Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
title_short	Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
title_full	Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
title_fullStr	Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
title_full_unstemmed	Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
title_sort	decision support system for hyperspectral remote-sensing data of yellow river estuary, china
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/cb040348bec444b597a9899c3095a96c
work_keys_str_mv	AT wangping decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT jiefu decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT wenyuqiao decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT muhammadyasir decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT shenghui decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT mdsakaouthhossain decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina AT shahnazir decisionsupportsystemforhyperspectralremotesensingdataofyellowriverestuarychina
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Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

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