IGSNRR OpenIR
Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network
Ling, Feng1,2; Boyd, Doreen3; Ge, Yong4; Foody, Giles M.3; Li, Xiaodong1,3; Wang, Lihui1; Zhang, Yihang1; Shi, Lingfei1; Shang, Cheng1; Li, Xinyan1; Du, Yun1,2
2019-07-01
Source PublicationWATER RESOURCES RESEARCH
ISSN0043-1397
Volume55Issue:7Pages:5631-5649
Corresponding AuthorLing, Feng(lingf@whigg.ac.cn)
AbstractRiver wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery, and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the subpixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by superresolution mapping (SRM). In the SRM analysis, a deep convolutional neural network is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the convolutional neural network-based SRM model can effectively estimate subpixel scale details of rivers and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery.
DOI10.1029/2018WR024136
WOS KeywordESTIMATING DISCHARGE ; MIXTURE ANALYSIS ; CARBON-DIOXIDE ; LAND ; STREAMS ; WATERLINE ; MODEL
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDA2003030201] ; National Science Fund for Distinguished Young Scholars of China[41725006] ; National Natural Science Foundation of China[51809250] ; Youth Innovation Promotion Association CAS[2017384]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; National Science Fund for Distinguished Young Scholars of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
WOS SubjectEnvironmental Sciences ; Limnology ; Water Resources
WOS IDWOS:000481444700026
PublisherAMER GEOPHYSICAL UNION
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69018
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLing, Feng
Affiliation1.Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan, Hubei, Peoples R China
2.Chinese Acad Sci, Sino Africa Joint Res Ctr, Wuhan, Hubei, Peoples R China
3.Univ Nottingham, Sch Geog, Nottingham, England
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Ling, Feng,Boyd, Doreen,Ge, Yong,et al. Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network[J]. WATER RESOURCES RESEARCH,2019,55(7):5631-5649.
APA Ling, Feng.,Boyd, Doreen.,Ge, Yong.,Foody, Giles M..,Li, Xiaodong.,...&Du, Yun.(2019).Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network.WATER RESOURCES RESEARCH,55(7),5631-5649.
MLA Ling, Feng,et al."Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network".WATER RESOURCES RESEARCH 55.7(2019):5631-5649.
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