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Remote Sensing Technology and Application  2022, Vol. 37 Issue (3): 731-738    DOI: 10.11873/j.issn.1004-0323.2022.3.0731
    
Research on Urban Water Body Extraction based on Transfer Learning of Three High-resolution Image Datasets
Jiarui Shi1(),Qian Shen2(),Hongchun Peng1,Liwei Li2,Yue Yao2,Mingxiu Wang2,Ru Wang1
1.School of Marine Technology and Geomatics,Jiangsu Ocean Unversity,Lianyungang 222005,China
2.Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
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Abstract  

Water extraction is an essential step for rare earth monitoring of urban water environment. Extraction of small water bodies in the city has now become a hot depth study in the field of remote sensing images. However, deep learning requires a large number of sample datasets as input, and images with different spatial resolutions often need to construct different datasets. If the spatial resolution of the images is not much different, the sample transfer learning model can be used to ensure accuracy and save time. In this paper, the U-Net image segmentation model is selected to perform sample transfer learning for images with three different spatial resolutions—0.5 m, 0.8 m and 2 m respectively. It is found that after three migration learning of 2 meters to 0.8 meters, 2 meters to 0.5 meters, and 0.8 meters to 0.5 meters, the corresponding evaluation indexes F1-score, MioU and Kappa of the extracted water body are all above 0.80. Under the premise of little difference in resolution, this method of extracting urban water bodies from lower-resolution samples to higher-resolution images is basically feasible, and the accuracy of the results is better. It is suitable for water extraction in water-deficient cities.

Key words:  High-resolution remote sensing image      Sample transfer learning      U-Net     
Received:  27 November 2020      Published:  25 August 2022
ZTFLH:  P332  
Corresponding Authors:  Qian Shen     E-mail:  2018224033@jou.deu.cn;shenqian@radi.ac.cn
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Articles by authors
Jiarui Shi
Qian Shen
Hongchun Peng
Liwei Li
Yue Yao
Mingxiu Wang
Ru Wang

Cite this article: 

Jiarui Shi,Qian Shen,Hongchun Peng,Liwei Li,Yue Yao,Mingxiu Wang,Ru Wang. Research on Urban Water Body Extraction based on Transfer Learning of Three High-resolution Image Datasets. Remote Sensing Technology and Application, 2022, 37(3): 731-738.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0731     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I3/731

Fig.1  Model construction
影像名称波段融合后空间分辨率/m成像时间中心点经纬度像元数量
Worldview-2R,G,B,NIR0.52017/9/19116.7N,39.8E16 022×32 769
Superview-1R,G,B,NIR0.52017/8/22116.8N,39.7E11 318×11 734
Geoeye-1R,G,B,NIR0.52017/5/20116.7N,39.8E11 214×11 637
BJ-2R,G,B,NIR0.82019/6/24116.8N,39.9E36 610×36 518
GF-2R,G,B,NIR0.82018/9/05116.7N,39.9E34 431×33 498
GF-1R,G,B,NIR22019/7/12116.7N,39.9E21 770×21 291
GF-1BR,G,B,NIR22019/9/24117.0N,39.7E40 269×40 028
GF-1CR,G,B,NIR22020/3/11117.0N,39.7E39 908×39 725
GF-1DR,G,B,NIR22019/10/21116.7N,39.7E41 347×40 289
Table 1  Remote sensing image parameters
Fig.2  High-resolution urban water body sample sets and raster label sample sets
Fig.3  Datasets division
Fig.4  U-net model structure
Fig.5  Improved Unet accuracy comparison
Fig.6  Five Model extract result indicators
Fig.7  Worldview-2 extract Small water body display
Fig.8  High-resolution image extraction results
Fig.9  Water extraction details display
Fig.10  Shenyang city water extraction results
Fig.11  Ningbo city water extraction results
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