SAR卫星影像洪水检测研究进展及展望

doi:10.12082/dqxxkx.2023.230060

Abstract

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

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

Figures/Tables 3

Tab. 1

Tab. 2

Classification of SAR flood detection methods and their characteristics, advantages and disadvantages, examples and key references

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]、

Tab. 2

Fig. 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

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

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