Information extraction of inland surface water bodies based on optical remote sensing:A review

doi:10.6046/zrzyyg.2023123

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Abstract

Inland surface water bodies, including rivers, lakes, and reservoirs, are significant freshwater resources for human beings and ecology, and their monitoring and control are greatly significant. Optical remote sensing provides great convenience for the monitoring of surface water resources, proving to be an important means for the information extraction and dynamic monitoring of inland surface water bodies. This study reviews the basic principles, remote sensing data sources, methods, existing issues, and prospects of the information extraction of water bodies. Owing to the unique characteristics of the remote sensing images of inland surface water bodies, their information can be extracted in an accurate, scientific, and effective manner using remote sensing. Multiple remote sensing data resources can be applied to the information extraction, and the optical remote sensing-based extraction methods include the threshold value method, classifier method, object orientation method, and deep learning method. Given that different methods have unique advantages, disadvantages, and applicable conditions, selecting appropriate multi-source data and varying methods based on the conditions of study areas tend to improve the information extraction accuracy. Nevertheless, there still exist some issues in the optical remote sensing-based water body information extraction, such as the balance of spatiotemporal resolution of remote sensing data, the information mining of water body characteristics, the generalization ability of water body models, and the uniformity of criteria for accuracy evaluation.

Keywords optical remote sensing water body extraction data source extraction method

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Issue Date: 03 September 2024

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	Siwei FENG
	Qinghua YANG
	Weijie JIA
	Mengfei WANG
	Lei LIU

Cite this article:

Siwei FENG,Qinghua YANG,Weijie JIA, et al. Information extraction of inland surface water bodies based on optical remote sensing:A review[J]. Remote Sensing for Natural Resources, 2024, 36(3): 41-56.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023123 OR https://www.gtzyyg.com/EN/Y2024/V36/I3/41

Tab.1 Common remote sensing data sources for water body extraction

名称	计算公式^①	参考文献	时间	优点	缺点
NDWI	$N D W I = G - N I R G + N I R$	McFeeters^[33]	1996年	能够有效增强水体光谱特征	容易混淆水体与城镇建筑信息
MNDWI	$M N D W I = G - M I R G + M I R$	徐涵秋^[34]	2005年	加强了水体与建筑物等噪声的反差	容易混淆水体和阴影
EWI	$E W I = G - N I R - M I R G + N I R + M I R$	闫霈等^[35]	2007年	可有效地区分半干涸河道与背景噪音	不适用于无中红外波段的传感器影像
NWI	$N W I = B - (N I R + S W I R 1 + S W I R 2) B + (N I R + S W I R 1 + S W I R 2) C$	丁凤^[36]	2009年	利用了水体在Landsat ETM+B7的强吸收特性	不适用于无中红外波段的传感器影像
AWEI_nsh/AWEI_sh	$A W E I n s h = 4 (G - S W I R 1) - (0.25 N I R + 2.75 S W I R 2)$ $A W E I s h = B + 2.5 G - 1.5 (N I R + S W I R 1) - 0.25 S W I R 2$	Feyisa等^[37]	2014年	可提供较稳定的分割阈值	不适用于如冰雪、白色建筑物等高反射率的地物
WI2015	$W I 2015 = 1.720 ? 4 + 171 G + 3 R - 70 N I R - 45 S W I R 1 - 71 S W I R 2$	Fisher等^[38]	2015年	可区分多种水体类型且遗漏误差较低	分类需要多层决策且阈值设定较为主观
MBWI	$M B W I = 2 R - N I R - S W I R 1 - T I R - S W I R 2$	王小标等^[39]	2018年	可用于提取复杂地表环境下的水体	不适用于细小水体
NDMBWI	$N D M B W I = 3 G - B + 2 R - 5 N I R 3 G + B + 2 R + 5 N I R$	邓开元等^[40]	2021年	可有效排除雪、云、阴影的影响	最优阈值不能一直保持在0
GRN-WI	$G R N W I = G + R - 2 N I R$	雷盛磊等^[41]	2022年	能够有效抑制冰雪、阴影等噪声	对于GF等数据源的有效性尚未验证
RWI	$R W I = (B 3 + B 5) - (B 8 + B 8 A + B 12) (B 3 + B 5) + (B 8 + B 8 A + B 12)$	吴庆双等^[42]	2019年	可以有效提取细小水体	需要用到植被红边波段,不适用于其他传感器
CWI	$C W I = 3 N I R - G - B$	王春霞等^[43]	2022年	能应对复杂环境,减少阴影、建筑物等影响	需要结合图像分割以抑制噪声
TWI	$T W I = 2.84 (B 5 - B 6) B 3 + B 12 + 1.25 (B 3 - B 2) - (B 8 - B 2) B 8 + 1.25 B 3 - 0.25 B 2$	Niu等^[44]	2022年	可提高提取以不同水量为特征的水体的准确性	针对Sentinel的光谱设计特点定制,对于其他数据源的适用性有待验证

Tab.2 Common water body indices and their advantages and disadvantages

类别	代表文献	方法	原理	数据源	实验结果精度评价	优点	缺点
阈值法	[29]	单波段法	基于光谱特征	Landsat TM	总体精度96.90%	简单快速	不能利用水体多个波段的光谱信息,易混入阴影噪声
	[30]	谱间关系法		Landsat TM	总体精度94.09%	适用于山地地区水体提取	容易混杂建筑物信息
	—	水体指数法		种类较多,见表2
分类器方法	[28]	SVM	基于特征融合	Sentinel-2	总体精度96.00%,Kappa系数0.919 9	理论基础坚实,方法成熟稳定	不同的核函数、参数和样本的选取对性能影响较大
	[65]	决策树		SPOT-5	检测率86.18%,虚警率13.82%	分类准则容易可视化,易于理解分析	容易发生过拟合,容易忽略数据集中属性的相互关联
	[1]	RF		Sentinel-2	总体精度97.91%	能够有效处理具有高维特征的输入样本,且不需要特征选择	小数据或者低维数据(特征较少的数据),可能不能产生很好的分类
	[54]	最大似然法	基于光谱特征	SPOT-5	总体精度92.70%,Kappa系数0.827 1	实行较为简单快速,其密度分布函数可以有效解释分类结果	仅适用于波段较少的数据,且对训练样本要求较高
		最小距离分类法			总体精度89.84%,Kappa系数0.739 2	原理简单,容易理解,计算速度快	只考虑每一类样本的均值,分类精度较低
		马氏距离分类法			总体精度85.16%,Kappa系数0.520 6	能够有效区分水体和背景	存在非水体被过度分类的问题
面向对象方法	[54]	面向对象方法	基于特征融合	SPOT-5	总体精度97.72%,Kappa系数0.944 5	可降低“同物异谱,异物同谱”的影响,抑制椒盐噪声	精度受到分割结果的质量和分类规则有效区分性的影响
深度学习方法	[80]	CNN	基于特征融合	ZY-3	准确率81.40%,错分率8.91%	可处理高维数据,特征分类效果较好	需要调参,需要大样本量,每个卷积层的物理含义不明确
	[85]	FCN	基于特征融合	GF-1	总体精度98.52%	通过多个卷积层提取特征	上采样过程对图像中的细节不敏感,可能导致小水体被忽略,水体的边界被平滑
		U-Net			总体精度98.18%	没有较深的层数,且分割效果优良	融合了太多由浅卷积层提取的低层特征,这些低层特征图可能与具有和水体相似光谱特征的噪声的错误有关
		DeepLab			总体精度98.18%	没有较深的层数,且分割效果优良	融合了太多由浅卷积层提取的低层特征,这些低层特征图可能与具有和水体相似光谱特征的噪声的错误有关
		DeepLab			总体精度91.82%	适用于复杂场景中的像素级分割	水体提取过程中,容易过度提取

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