Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (10): 1933-1953.doi: 10.12082/dqxxkx.2023.230060
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GAO Hanxin1,2(), CHEN Bo1,2,*(
), SUN Hongquan3, TIAN Yugang4
Received:
2023-02-12
Revised:
2023-05-07
Online:
2023-10-25
Published:
2023-09-22
Contact:
* CHEN Bo, E-mail: Supported by:
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
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Tab. 1
Available SAR satellite data information for flood detection
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 |
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 |
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 |
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[ 文献[ |
非参数化 | 不作先验分布假设 | 大津算法(OTSU),水体指数提取法 | 文献[ 文献[ | |||
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[ |
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[ 文献[ | |
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[ | |
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[ | |
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[ | |
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[ | |
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[ |
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