牛世林1,
1.河南大学计算机与信息工程学院 开封 475004
2.河南省智能技术与应用工程技术研究中心 开封 475004
3.河南省大数据分析与处理重点实验室 开封 475004
基金项目:国家自然科学基金(U1604145, 61871175, 61601437)，河南省高等学校重点科研项目(18B520010, 19A420005)，河南省科技攻关计划项目(182102210233, 192102210082)，河南省青年人才托举工程(2019HYTP006)，河南大学研究生教育创新与质量提升计划项目(SYL18060127)
详细信息
作者简介:李宁：李 宁(1987–)，男，安徽人，毕业于中国科学院电子学研究所，获得博士学位，现为河南大学教授，研究方向为多模式合成孔径雷达成像及其应用技术。E-mail: lining_nuaa@163.com
牛世林(1993–)，男，河南人，河南大学计算机与信息工程学院硕士研究生，主要研究方向为合成孔径雷达图像处理及其应用技术。E-mail: nsl1993@foxmail.com
通讯作者:李宁 lining_nuaa@163.com
中图分类号:TN959.1; TP183计量
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被引次数:0
出版历程
收稿日期:2019-11-06
修回日期:2020-02-02
网络出版日期:2020-02-27
High-precision Water Segmentation from Synthetic Aperture Radar Images Based on Local Super-resolution Restoration Technology
LI Ning1,2,3,,,NIU Shilin1,
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
摘要
摘要:合成孔径雷达(SAR)图像水域分割在水资源调查、灾害监测等领域具有重要意义。针对中低分辨率星载SAR图像水域提取精度不足的难题，该文融合基于轻量级残差卷积神经网络(CNN)的图像超分辨率重建技术和传统SAR图像水域分割技术的优点，提出了一种基于局部超分辨重建的SAR图像水域分割方法，显著提升了SAR图像水域分割的精度。为了验证上述方法的有效性，该文以南水北调中线工程水源地丹江口水库为应用对象，基于国产高分三号(GF-3)卫星的8 m分辨率标准条带(SS)模式图像和欧空局Sentinel-1卫星20 m分辨率干涉宽幅(IW)模式图像，开展了水域分割的实验验证和精度评估工作。实验结果表明，该文所提方法可在中低分辨率SAR图像中获取更精确的水域分割结果，其水域分割性能较传统方法有大幅提升。
关键词:合成孔径雷达/
水域分割/
卷积神经网络/
超分辨率重建
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.
Key words:Synthetic Aperture Radar (SAR)/
Water segmentation/
Convolutional Neural Network (CNN)/
Super-resolution restoration
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