Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery

Urban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring. In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, m...

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Main Authors: Lifei Wei, Zhou Wang, Can Huang, Yu Zhang, Zhengxiang Wang, Huiqiong Xia, Liqin Cao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9195460/
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spelling doaj-d62b8c54c5224e43bee831857a1707b12021-03-30T03:43:17ZengIEEEIEEE Access2169-35362020-01-01816813716815310.1109/ACCESS.2020.30236909195460Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing ImageryLifei Wei0Zhou Wang1https://orcid.org/0000-0002-3733-8916Can Huang2Yu Zhang3Zhengxiang Wang4Huiqiong Xia5Liqin Cao6https://orcid.org/0000-0003-2945-2708Faculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan, ChinaSchool of Printing and Packaging, Wuhan University, Wuhan, ChinaUrban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring. In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, making fine pollution monitoring of urban rivers difficult. Transparency is one of the core indicators for evaluating water quality, and hyperspectral remote sensing data are rich in spectral information and can be used for quantitative transparency estimation. The application of unmanned aerial vehicles (UAV)remote sensing effectively makes up for the deficiencies in satellite remote sensing monitoring. Aiming at this problem, this paper proposed the use of the eXtreme Gradient Boosting (XGBoost) regression algorithm for the quantitative inversion of urban river transparency. The spatial resolution of the collected imagery is 18.5 cm, which is suitable for urban rivers that are almost ten meters wide. Compared with five traditional empirical models, integrated algorithms such as gradient regression and random forest get much better results. Moreover, the accuracy of transparency estimation using the XGBoost regression algorithm was significantly improved, and the inversion model R<sup>2</sup> in both study areas reached over 0.97. Finally, the established transparency inversion models were used to generate transparency distribution maps of the two study areas. The results showed that the distribution of the water transparency was consistent with the results of the field monitoring, indicating that it is feasible to use the XGBoost algorithm for the inversion of urban river transparency in UAV-borne hyperspectral imagery.https://ieeexplore.ieee.org/document/9195460/TransparencyUAV-borneextreme gradient boostinghyperspectral imagery
collection DOAJ
language English
format Article
sources DOAJ
author Lifei Wei
Zhou Wang
Can Huang
Yu Zhang
Zhengxiang Wang
Huiqiong Xia
Liqin Cao
spellingShingle Lifei Wei
Zhou Wang
Can Huang
Yu Zhang
Zhengxiang Wang
Huiqiong Xia
Liqin Cao
Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
IEEE Access
Transparency
UAV-borne
extreme gradient boosting
hyperspectral imagery
author_facet Lifei Wei
Zhou Wang
Can Huang
Yu Zhang
Zhengxiang Wang
Huiqiong Xia
Liqin Cao
author_sort Lifei Wei
title Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
title_short Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
title_full Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
title_fullStr Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
title_full_unstemmed Transparency Estimation of Narrow Rivers by UAV-Borne Hyperspectral Remote Sensing Imagery
title_sort transparency estimation of narrow rivers by uav-borne hyperspectral remote sensing imagery
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Urban rivers are often narrow, and general remote sensing data cannot meet the needs of water quality monitoring. In the process of monitoring of river water quality by remote sensing, the spectral and spatial dimension of satellite-borne images cannot be taken into consideration at the same time, making fine pollution monitoring of urban rivers difficult. Transparency is one of the core indicators for evaluating water quality, and hyperspectral remote sensing data are rich in spectral information and can be used for quantitative transparency estimation. The application of unmanned aerial vehicles (UAV)remote sensing effectively makes up for the deficiencies in satellite remote sensing monitoring. Aiming at this problem, this paper proposed the use of the eXtreme Gradient Boosting (XGBoost) regression algorithm for the quantitative inversion of urban river transparency. The spatial resolution of the collected imagery is 18.5 cm, which is suitable for urban rivers that are almost ten meters wide. Compared with five traditional empirical models, integrated algorithms such as gradient regression and random forest get much better results. Moreover, the accuracy of transparency estimation using the XGBoost regression algorithm was significantly improved, and the inversion model R<sup>2</sup> in both study areas reached over 0.97. Finally, the established transparency inversion models were used to generate transparency distribution maps of the two study areas. The results showed that the distribution of the water transparency was consistent with the results of the field monitoring, indicating that it is feasible to use the XGBoost algorithm for the inversion of urban river transparency in UAV-borne hyperspectral imagery.
topic Transparency
UAV-borne
extreme gradient boosting
hyperspectral imagery
url https://ieeexplore.ieee.org/document/9195460/
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