SAR卫星影像洪水检测研究进展及展望

doi:10.12082/dqxxkx.2023.230060

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

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.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. 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/Abstract

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

高寒新, 陈波, 孙洪泉, 田玉刚. SAR卫星影像洪水检测研究进展及展望[J]. 地球信息科学学报, 2023, 25(10): 1933-1953.DOI:10.12082/dqxxkx.2023.230060

GAO Hanxin, CHEN Bo, SUN Hongquan, TIAN Yugang. Research Progress and Prospect of Flood Detection Based on SAR Satellite Images[J]. Journal of Geo-information Science, 2023, 25(10): 1933-1953.DOI:10.12082/dqxxkx.2023.230060

图/表 3

表1

表2

SAR洪水检测方法分类及其特征、优缺点、例子与关键文献

方法	特征		优点	缺点	例子	关键文献
阈值法	参数化	事先对地物类别统计分布做假设，计算统计模型参数	方法简单，计算效率高	过度依赖直方图的双峰分布性，受环境异质性影响大	单峰分布拟合（伽马），多峰分布拟合（高斯）	文献[18]、文献[19]、文献[20]、文献[21]
阈值法	非参数化	不作先验分布假设	方法简单，计算效率高	过度依赖直方图的双峰分布性，受环境异质性影响大	大津算法（OTSU）,水体指数提取法	文献[15⇓⇓⇓⇓⇓⇓]、文献[22]、文献[23]、文献[24]
分类器法	监督分类	需地物相应像素组成的训练集	不需在设计算法前深入了解影像地物的散射特性，控制训练样本的选择	训练集的生成很难自动化，水体内部的方差值越大，样本代表性越差	随机森林分类器，支撑向量机	文献[25]、文献[26]、
	非监督分类	不施加任何的先验知识，算法自学习并进行聚类	不需先验知识，人为误差机会减小	“同物异谱”及“异物同谱”现象使分类集群与地类间不对应	ISODATA，K-means聚类	文献[27]、文献[28]、文献[29]
	半监督分类	已分类与未分类数据一起参与分类器训练	弥补监督学习样本泛化性不足，提高非监督学习模型精度	训练模型过程较复杂，时间成本较大	卷积神经网络（CNN）快速洪水范围制图法	文献[30]、文献[31]、
	面向对象	基于包含重要语义信息在内的对象及其间的相互关系训练分类器	充分利用除辐射信息外的对象信息（例形状），有效解决影像的椒盐噪声问题	模型较复杂，地物分割尺寸影响分类精度	形态语义分割	文献[32]、文献[33]、
变化检测	需洪水前（非淹没）影像，多与阈值分割技术配合使用		有效限制过度检测与影像几何误差	受影像条件限制大，受斑点噪音影响大	比值影像，差值影像	文献[18]、文献[19]、
干涉测量	根据相干性值的高低区分淹没与非淹没区		限制建筑物的双重反射效应对洪水识别的干扰	相干性对时间基线非常敏感，强度信息与相位信息同时计算复杂	CSK强度图与相干图RGB通道组合	文献[34]、文献[35]、
时间序列	基于时间序列数据获取动态淹没图		减弱洪水信息与物候周期有关的植被地物的混淆	数据采样频率不高、存储空间要求高	NDFI和NDFVI的阈值法	文献[36]、文献[37]、

表2

图1

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工作波段	SAR数据	扫描宽度/km	分辨率/m	重复周期/d	国家/机构	工作年限/年
L	ALOS-PALSAR1	40~350	7~14~100	46	日本	2006—2011
L	ALOS-PALSAR2	25/35/60/70/350	1/3/6/10/100	14	日本	2006—2011
C	ERS-1	100	30	35	欧空局	1991—2000
	ERS-2	100	30	35	欧空局	1995—2010
	ENVISAT-ASAR	100~400	20/70/150	35	欧空局	2002—2012
	RADARSAT-1	45~500	8~100	24	加拿大	1995—2013
	RADARSAT-2	15~500	3~100	24	加拿大	2007—至今
	Sentinel-1	20/80/250/400	5/20/40	12	欧空局	2014—至今
	GF3	10~650	1~500	<3	中国	2016—至今
	HISEA-1	5~100	1	3	中国	2020—至今
X	TerraSAR-X	5~10~30~100	1~3~16	11	德国	2007—至今
X	COSMO-SkyMed	10~30~200	1~3~15	16	意大利	2007—至今
Ku	Qilu-1	500	0.5	-	中国	2021—至今

SAR卫星影像洪水检测研究进展及展望

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

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