Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning
文献类型：期刊论文
| 作者 | Chen, Yang1,2; Fan, Rongshuang2; Yang, Xiucheng3; Wang, Jingxue1; Latif, Aamir4 |
| 刊名 | WATER
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 10期号:5页码:20 |
| 关键词 | deep learning convolutional neural networks superpixel urban water bodies high-resolution remote-sensing images |
| ISSN号 | 2073-4441 |
| DOI | 10.3390/w10050585 |
| 通讯作者 | Chen, Yang(chenyang1017@126.com) |
| 英文摘要 | Accurate information on urban surface water is important for assessing the role it plays in urban ecosystem services in the context of human survival and climate change. The precise extraction of urban water bodies from images is of great significance for urban planning and socioeconomic development. In this paper, a novel deep-learning architecture is proposed for the extraction of urban water bodies from high-resolution remote sensing (HRRS) imagery. First, an adaptive simple linear iterative clustering algorithm is applied for segmentation of the remote-sensing image into high-quality superpixels. Then, a new convolutional neural network (CNN) architecture is designed that can extract useful high-level features of water bodies from input data in a complex urban background and mark the superpixel as one of two classes: an including water or no-water pixel. Finally, a high-resolution image of water-extracted superpixels is generated. Experimental results show that the proposed method achieved higher accuracy for water extraction from the high-resolution remote-sensing images than traditional approaches, and the average overall accuracy is 99.14%. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; OF-THE-ART ; SCENE CLASSIFICATION ; SLIC SUPERPIXELS ; SATELLITE IMAGES ; INDEX NDWI ; SENTINEL-2 ; MANAGEMENT ; FEATURES ; BODY |
| 资助项目 | Nation key R&D Program of China[2016YFC0803100] ; National Natural Science Foundation of China[41101452] ; Doctoral Program Foundation of Institutions of Higher Education of China[20112121120003] |
| WOS研究方向 | Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000435196700051 |
| 出版者 | MDPI |
| 资助机构 | Nation key R&D Program of China ; National Natural Science Foundation of China ; Doctoral Program Foundation of Institutions of Higher Education of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/54663]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Yang |
| 作者单位 | 1.Liaoning Tech Univ, Sch Geomat, Fuxing 123000, Peoples R China 2.Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China 3.Univ Strasbourg, ICube Lab, F-67000 Strasbourg, France 4.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 10010, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yang,Fan, Rongshuang,Yang, Xiucheng,et al. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning[J]. WATER,2018,10(5):20. |
| APA | Chen, Yang,Fan, Rongshuang,Yang, Xiucheng,Wang, Jingxue,&Latif, Aamir.(2018).Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning.WATER,10(5),20. |
| MLA | Chen, Yang,et al."Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning".WATER 10.5(2018):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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