Volume 9 Issue 1
Feb.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(1): 174-184
LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096
Citation: LI Ning and NIU Shilin. High-precision water segmentation from synthetic aperture radar images based on local super-resolution restoration technology[J]. Journal of Radars, 2020, 9(1): 174–184. doi: 10.12000/JR19096
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High-precision Water Segmentation from Synthetic Aperture Radar Images Based on Local Super-resolution Restoration Technology

DOI: 10.12000/JR19096
  • 1.

    College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

  • 2.

    Henan Engineering Research Center of Intelligent Technology and Application, Kaifeng 475004, China

  • 3.

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China

Funds:  The National Natural Science Foundation of China (U1604145, 61871175, 61601437), The College Key Research Project of Henan Province (18B520010, 19A420005), The Plan of Science and Technology of Henan Province (182102210233, 192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), The Graduate Education Innovation and Quality Improvement Program of henan University (SYL18060127)
More Information
  • Corresponding author: LI Ning, lining_nuaa@163.com
  • Received Date: 2019-11-06
  • Rev Recd Date: 2020-02-02
    • Available Online: 2020-02-27
  • Publish Date: 2020-02-28
    Abstract
  • The extraction of water from Synthetic Aperture Radar (SAR) images is of great significance in water resources investigation and monitoring disasters. To deal with the problems of the insufficient accuracy of water boundaries extracted from middle-low resolution SAR images. This paper proposes a high-precision water boundaries extraction method based on a local super-resolution restoration technology that combines the advantages of the super-resolution restoration technology based on the lightweight residual Convolutional Neural Network (CNN) and the traditional SAR images water extraction methods. The proposed method can significantly improve the accuracy of water segmentation results by using SAR images. To verify the effectiveness of the proposed method, as a study area, we selected the Danjiangkou Reservoir, the water source of the middle route of a south-to-north water diversion project. Further, we conducted experiments on the multi-mode SAR dataset and evaluated its accuracy. This dataset included one Standard Strip-map (SS) mode image obtained by the Chinese GaoFen-3 (GF-3) satellite with a resolution of 8 m and one Interferometric Wide-swath (IW) mode SAR image obtained by Sentinel-1 satellite with a resolution of 20 m. The experimental results showed that the water segmentation results from the middle–low resolution SAR images of the proposed method were more precise, and the overall water segmentation performance was superior to that of the traditional methods.

     

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