A High-precision Water Segmentation Algorithm for SAR Image and its Application

Jiaqi CHEN; Xiangmei LIU; Ning LI; Yan ZHANG

doi:10.11999/JEIT200366

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 700-707

Jiaqi CHEN, Xiangmei LIU, Ning LI, Yan ZHANG. A High-precision Water Segmentation Algorithm for SAR Image and its Application[J]. Journal of Electronics & Information Technology, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366

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Jiaqi CHEN, Xiangmei LIU, Ning LI, Yan ZHANG. A High-precision Water Segmentation Algorithm for SAR Image and its Application[J]. Journal of Electronics & Information Technology, 2021, 43(3): 700-707. doi: 10.11999/JEIT200366

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A High-precision Water Segmentation Algorithm for SAR Image and its Application

doi: 10.11999/JEIT200366

Jiaqi CHEN^{1
,
,},
Xiangmei LIU¹,
Ning LI^{2, 3},
Yan ZHANG¹

1.
College of Computer and Information, Hohai University, Nanjing 211100, China
2.
School of Computer and Information Engineering, Henan University, 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 (61771183, 61601437), The Fundamental Research Funds for the Central University (2016B07114), The Plan of Science and Technology of Henan Province (192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006), China Postdoctoral Science Foundation (2013M541035)

Received Date: 2020-05-08
Rev Recd Date: 2020-12-05

Available Online: 2020-12-17

Publish Date: 2021-03-22

Abstract

Abstract

Water segmentation of Synthetic Aperture Radar (SAR) is of great significance in land hydrological monitoring, such as lakes and rivers. Water segmentation accuracy is influenced by the blurring of the boundary between land and water region because of the insufficient resolution of SAR image. Sentinel-1A SAR image is used to study the Duoqing Co in the Tibetan Plateau of China. This paper integrates the enhanced deep residual block, channel attention mechanism and sub-pixel convolution, an enhanced channel attention deep residual network is proposed based on sub-pixel to reconstruct the filtered SAR image, extract the water contour and analyze the accuracy. By comparing the reconstruction results of different super-resolution algorithms and the accuracy of water contour extraction, this algorithm, with great robustness, is obviously better than the traditional method in both reconstruction effect and extraction accuracy.
- Synthetic Aperture Radar (SAR),
- Super-resolution reconstruction,
- Water segmentation

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References

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