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UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring

文献类型：期刊论文

作者Liu, Hong2,3,4,5; Yu, Tao2,4,5; Hu, Bingliang2,4,5; Hou, Xingsong3; Zhang, Zhoufeng2,4; Liu, Xiao2,4; Liu, Jiacheng2,4,5; Wang, Xueji2,4; Zhong, Jingjing5; Tan, Zhengxuan1
刊名REMOTE SENSING
出版日期2021-10-01
卷号13期号:20页码:32
关键词hyperspectral imaging acousto-optic tunable filter UAV platform remote sensing water quality monitoring
DOI10.3390/rs13204069
通讯作者Yu, Tao(yutao@opt.ac.cn)
英文摘要Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air-ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air-ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 mu g/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.

WOS关键词CHLOROPHYLL-A CONCENTRATION ; AERIAL VEHICLE UAV ; AQUATIC VEGETATION ; RANDOM FOREST ; CALIBRATION ; ALGORITHMS ; RETRIEVAL ; MODEL ; SPECTROMETER ; INFORMATION
资助项目class a plan of major strategic pilot project of the Chinese Academy of Sciences[XDA23040101] ; National Natural Science Foundation of China[61872286] ; National Natural Science Foundation of China[11727806] ; Key R&D Program of Shaanxi Province of China[2020ZDLGY04-05] ; Key R&D Program of Shaanxi Province of China[S2021-YF-YBSF-0094] ; National Key R&D Program of China[2017YFC1403700] ; Light of the west project of Chinese Academy of Sciences[XAB2017B25]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000715609800001
资助机构class a plan of major strategic pilot project of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Key R&D Program of Shaanxi Province of China ; National Key R&D Program of China ; Light of the west project of Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/167430]  
专题中国科学院地理科学与资源研究所
通讯作者Yu, Tao
作者单位1.Univ Miami, Dept Comp Sci, Miami, FL 33136 USA
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
3.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China
4.Chinese Acad Sci, Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China
5.Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
7.Bur Hydrol Changjiang Water Resources Commiss CWR, Wuhan 443010, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Hong,Yu, Tao,Hu, Bingliang,et al. UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring[J]. REMOTE SENSING,2021,13(20):32.
APA Liu, Hong.,Yu, Tao.,Hu, Bingliang.,Hou, Xingsong.,Zhang, Zhoufeng.,...&Qian, Bao.(2021).UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring.REMOTE SENSING,13(20),32.
MLA Liu, Hong,et al."UAV-Borne Hyperspectral Imaging Remote Sensing System Based on Acousto-Optic Tunable Filter for Water Quality Monitoring".REMOTE SENSING 13.20(2021):32.

入库方式： OAI收割

来源：地理科学与资源研究所

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