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Learning vector quantization neural network for surface water extraction from Landsat OLI images

文献类型：期刊论文

作者Somasundaram, Deepakrishna1,2; Zhang, Fangfang1; Wang, Shenglei1,3; Ye, Huping4; Zhang, Zongke5,6; Zhang, Bing1,2
刊名JOURNAL OF APPLIED REMOTE SENSING
出版日期2020-01-14
卷号14期号:3页码:15
关键词water extraction water body mapping learning vector quantization neural network machine learning Landsat 8
ISSN号1931-3195
DOI10.1117/1.JRS.14.032605
通讯作者Zhang, Bing(zb@radi.ac.cn)
英文摘要There is a growing concern over surface water dynamics due to an increased understanding of water availability and management with current climate trends. Remote sensing has now become an effective means of water extraction due to the availability of an enormous amount of data with diverse spatial, spectral, and temporal resolutions. However, water extraction from optical remote sensing data is associated with several major difficulties, such as the applicability of the extraction method over large areas and complex environments; shadow contamination from clouds, buildings, and mountains; and disclosure of shadowed water and exclusion of floating and submerged plants. To address these difficulties, a learning vector quantization (LVQ) neural network-based method was proposed and implemented to extract water using Landsat 8 imageries. This method is capable of separating water from clouds, build-up areas, shadows, and shadowed water by the ideal input of bands 1 to 7 and normalized difference vegetation index. This model learns water across Sri Lanka. Eight OLI scenes were tested, and the performance was compared with five widely used machine learning algorithms: support vector machine, K-nearest neighbor, discriminant analysis, combination of modified normalized difference water index and modified fuzzy clustering method, and K-means clustering methods. This method performed the best, achieving overall accuracies and the kappa coefficients between 97.8% and 99.7% and between 0.96 and 0.99, respectively. Results have demonstrated robustness, consistency, and preciseness in various dark surfaces, noisiest water environments, and highly water scarce scenes. LVQ revealed a good generalizing ability to detect all types of water with less amount of training samples. This method can be easily adaptable for other sensors and global water to support water resource studies. (C) The Authors.
WOS关键词CLASSIFICATION ; ALGORITHMS ; COASTAL ; FUSION
资助项目National Natural Science Foundation of China[41701402] ; National Natural Science Foundation of China[91638201] ; National Natural Science Foundation of China[41471308] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19080304] ; CAS-TWAS President's PhD Fellowship Program
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000551462900001
出版者SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; CAS-TWAS President's PhD Fellowship Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/158300]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Bing
作者单位1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Chinese Acad Sci, China Sri Lanka Joint Res & Demonstrat Ctr Water, Beijing, Peoples R China
6.Chinese Acad Sci, China Sri Lanka Joint Ctr Educ & Res, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Somasundaram, Deepakrishna,Zhang, Fangfang,Wang, Shenglei,et al. Learning vector quantization neural network for surface water extraction from Landsat OLI images[J]. JOURNAL OF APPLIED REMOTE SENSING,2020,14(3):15.
APA Somasundaram, Deepakrishna,Zhang, Fangfang,Wang, Shenglei,Ye, Huping,Zhang, Zongke,&Zhang, Bing.(2020).Learning vector quantization neural network for surface water extraction from Landsat OLI images.JOURNAL OF APPLIED REMOTE SENSING,14(3),15.
MLA Somasundaram, Deepakrishna,et al."Learning vector quantization neural network for surface water extraction from Landsat OLI images".JOURNAL OF APPLIED REMOTE SENSING 14.3(2020):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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