地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 1933-1953.doi: 10.12082/dqxxkx.2023.230060

• 综述 • 上一篇    下一篇

SAR卫星影像洪水检测研究进展及展望

高寒新1,2(), 陈波1,2,*(), 孙洪泉3, 田玉刚4   

  1. 1.北京师范大学环境演变与自然灾害教育部重点实验室,北京师范大学地理科学学部,北京 100875
    2.北京师范大学地表过程与资源生态国家重点实验室,北京 100875
    3.应急管理部-国家自然灾害防治研究院,北京 100085
    4.中国地质大学(武汉)地理与信息工程学院,武汉 430078
  • 收稿日期:2023-02-12 修回日期:2023-05-07 出版日期:2023-10-25 发布日期:2023-09-22
  • 通讯作者: * 陈波(1984—),男,湖北红安人,副教授,主要从事地表水文系统与洪水风险评估方面的研究。 E-mail: bochen@bnu.edu.cn
  • 作者简介:高寒新(1997—),女,山东淄博人,硕士生,主要从事SAR影像洪水检测方面的研究。E-mail: ghx@mail.bnu.edu.cn
  • 基金资助:
    国家重点研发计划专项(2021YFB3901203)

Research Progress and Prospect of Flood Detection Based on SAR Satellite Images

GAO Hanxin1,2(), CHEN Bo1,2,*(), SUN Hongquan3, TIAN Yugang4   

  1. 1. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    3. National Institute of Natural Disaster Prevention and Control, Ministry of Emergency Management, Beijing 100085, China
    4. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
  • Received:2023-02-12 Revised:2023-05-07 Online:2023-10-25 Published:2023-09-22
  • Contact: * CHEN Bo, E-mail: bochen@bnu.edu.cn
  • Supported by:
    National Key Research and Development Project(2021YFB3901203)

摘要:

雷达卫星能够穿透云雾,全天时、全天候获取洪涝灾害期间地面的水体信息,被广泛应用于洪水检测。本文从合成孔径雷达(SAR)卫星数据源、洪水检测方法、辅助信息在洪水检测中的应用、精度评价、SAR洪水检测的挑战与发展方向六个方面,总结分析了国内外SAR影像洪水检测领域的研究进展。首先重点梳理了基于阈值分割、分类器和变化检测等洪水检测方法,发现阈值法计算速度快、应用广泛,分类器法可以充分发挥主观性和客观性,变化检测法可有效限制过度检测与影像几何误差。接着分析了光学遥感影像、地形、纹理、水文、土地覆盖/利用等辅助信息在洪水检测中的应用,发现SAR影像结合辅助数据进行洪水检测成为研究热点。然后综合洪水遥感检测的应用需求和SAR水体信息提取技术发展历程,分析了洪水检测在影像数据源、预处理、检测算法及精度验证等方面面临的挑战及原因,总结出目前SAR洪水检测的发展方向:即SAR洪水检测辅助信息的广泛应用、同一SAR影像洪水的差异化识别和基于SAR影像的洪水概率制图等。

关键词: SAR, 洪水检测, 阈值法, 变化检测, 辅助信息, 洪水概率图, 后向散射, 洪水风险

Abstract:

Being able to penetrate clouds and fog, Synthetic Aperture Radar (SAR) imagery has been widely used in flood mapping and flood detection regardless of time and weather condition. Improving the accuracy of flood maps retrieved from SAR images is of both scientific and practical significance. However, errors in SAR-derived flood maps can come from SAR image measuring principles, image acquisition and pre-processing system, water detection algorithms, and the remarkable temporal dynamics of the flooding process. The aim of this paper is to provide an extensive literature review of flood detection using SAR images (about 108 peer reviewed journal papers), including SAR data sources, flood detection methods, application of auxiliary information, accuracy evaluation, and challenges and opportunities for future research. Based on the articles reporting flood detection methods, it is found that the threshold segmentation methods such as the OTSU and KI algorithms are computationally fast and have been most widely used. The classification methods (e.g., the support vector machine and K-means clustering algorithms) have the flexibility to account for both subjectivity and objectivity, and the change detection method using the difference and ratio algorithms can effectively suppress over-detection and image geometric errors. Additionally, combining SAR images with four major types of auxiliary data to increase flood detection accuracy has become a hot topic in the past decades. Specifically, terrain information such as Digital Elevation Model (DEM), Height Above Nearest Drainage (HAND), and topographic slope can effectively reduce the impacts of shadows and exclude non-flooded areas. SAR image textural and multispectral optical information (e.g., Landsat data and aerial photos) can enhance the recognition ability of water features. Land cover/use data facilitate removing non-water features that are similar to water features, and hydrological data can help excluding permanent water bodies from temporary flood areas. From the perspectives of SAR image types, image preprocessing, detection algorithms, and accuracy assessment, major challenges are further discussed including insufficient understanding of the complexity of SAR backscattering information, limited progress in improving the signal-to-noise ratio during image pre-processing, lack of versatile flood detection algorithms, and low availability of high-quality verification data. While opportunities for future SAR-based flood detection research include combination of auxiliary information in detection algorithms, use of multiple rather than single threshold for water detection, and transition from deterministic toward probabilistic flood mapping.

Key words: Synthetic Aperture Radar (SAR), flood detection, threshold method, change detection, auxiliary information, flood probability map, backscatter, flood risk