基于高空间分辨率遥感影像的水深反演有效性评估

王燕茹; 张利勇; 刘文; 张凯; 王鑫

doi:10.12284/hyxb2023026

基于高空间分辨率遥感影像的水深反演有效性评估

doi: 10.12284/hyxb2023026

王燕茹^1,,
张利勇¹,
刘文¹,
张凯^{1, 2, ,},
王鑫^{1, 3}

1.
山东科技大学测绘与空间信息学院，山东青岛 266590
2.
自然资源部第二海洋研究所，浙江杭州 310012
3.
广州三海海洋工程勘察设计有限公司，广东广州 510220

基金项目: 山东省自然科学基金（ZR2020MD084）；山东科技大学科研创新团队支持计划（2019TDJH103）。

详细信息

作者简介:
王燕茹（1998-），女，山东省济南市人，主要从事海洋水深反演研究。E-mail：920635744@qq.com

通讯作者:
张凯（1983-），男，副教授，主要从事海洋测绘相关研究。E-mail：zk0773@163.com

① a图审图号为GS（2016）1585号。
中图分类号: TP79
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出版历程
- 收稿日期: 2022-05-10
- 修回日期: 2022-08-25
- 网络出版日期: 2022-09-05
- 刊出日期: 2023-02-01

Evaluation of validity of bathymetry retrieval data based on high-spatial resolution remote sensing image

Wang Yanru^1
,,
Zhang Liyong¹,
Liu Wen¹,
Zhang Kai^{1, 2
, ,},
Wang Xin^{1, 3}

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
3.
Guangzhou Sanhai Marine Engineering Surveying & Designing Co., Ltd., Guangzhou 510220, China

摘要

摘要: 卫星遥感反演水深（Satellite Derived Bathymetry, SDB）是获取浅海水深信息的有效手段。然而，其有效范围只限于光学浅水区域，在深水区域呈现“伪浅海”的失真现象。因此，如何准确识别SDB数据的有效范围对其广泛应用至关重要。本文基于高空间分辨率多光谱卫星影像，在深入分析深/浅水辐射亮度统计分布特征差异的基础上，提出一种数据驱动的水深反演有效性评价方法。该方法以卫星影像辐亮度信息的局域标准差作为特征，基于K-S检验方法对光学深水区域统计特征进行模型优选，并使用假设检验方法对深水无效区域对应的SDB进行识别。甘泉岛水域实验结果表明，该方法通过统计分布划分光学浅水与深水区域边界，可以有效识别光学深水区域产生的无效水深反演数据。在剔除无效区域数据后，光学浅水有效区域内水深反演平均绝对误差（MAE）为1.01，均方根误差（RMSE）为1.52。实验结果表明，本文提出的方法可准确识别SDB结果的有效区域，进而为浅海地形解译提供方法支撑。
- 水深反演 /
- 数据有效性 /
- 伪浅海 /
- 光学浅水区域
Abstract: Satellite derived bathymetric using multispectral imagery is an effective means to obtain shallow water depth information. However, its validity is limited to optical shallow water areas, but presents a “pseudo-shallow sea” distortion phenomenon in deep water areas. Therefore, accurately identifying the valid region of satellite derived bathymetry (SDB) data is crucial for its wide application. Based on high-spatial resolution remote sensing image, a data-driven method for evaluating the validity of SDB based on analysis of the differences in the statistical distribution of radiance in deep/shallow water regions is proposed in this paper. This method uses the local standard deviation of the radiance information of satellite images as a feature, optimizes the statistical characteristics of the optical deep water area based on the K-S test method, and uses the hypothesis test method to identify the SDB corresponding to the deep water invalid area. The experimental results in Ganquan Island region show that the method can effectively identify the invalid SDB associated with the optical deep water area by dividing the boundary between optical shallow and deep water area. After removing the invalid data, the mean absolute error (MAE) of SDB in the optical shallow region is 1.01, and the root mean square error (RMSE) is 1.52. The experimental results show that the proposed method can accurately identify the optical shallow region of SDB result, which benefits the interpretation and application of SDB results.
- bathymetric retrieval /
- data validity /
- pseudo-shallow sea /
- optical shallow water area
注释:

1) ① a图审图号为GS（2016）1585号。

HTML全文

图 1 研究区域概况

a. 永乐环礁地理位置^①；b. 永乐环礁卫星影像；c. 甘泉岛卫星影像

Fig. 1 Overview of the study area

a. Location of the Yongle Atoll; b. satellite image of Yongle Atoll; c. satellite image of Ganquan Island

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图 2 GeoEye-1遥感影像（a）、实测水深数据（b）和 Stumpf模型反演水深（c）

Fig. 2 GeoEye-1 sensing image (a), in-situ bathymetry data (b) and satellite derived bathymetry based on Stumpf model (c)

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图 3 水深光学遥感示意图

Fig. 3 Schematic diagram of optical bathymetry remote sensing

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图 4 光学深/浅水区域辐亮度统计分布特征图

Fig. 4 Statistical distribution map of radiance in the optical shallow/deep water region

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图 5 算法整体流程图

Fig. 5 The flow chart of the proposed algorithm

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图 6 蓝、绿波段辐亮度数据及对应标准差特征分布

a. 蓝波段辐亮度；b. 绿波段辐亮度；c. 蓝波段辐亮度标准差特征分布；d. 绿波段辐亮度标准差特征分布

Fig. 6 Radiance data of blue/green bands and distribution of associated standard deviation features

a. Radiance of blue band; b. radiance of green band; c. distribution of radiance standard deviation feature of blue band; d. distribution of radiance standard deviation feature of green band

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图 7 辐亮度标准差统计分布

Fig. 7 Radiance standard deviation statistical distribution

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图 8 不同波段深/浅水区域划分阈值及划定的光学深水区域

a. 蓝波段划分阈值；b. 蓝波段确定深水区域；c. 绿波段划分阈值；d. 绿波段确定深水区域

Fig. 8 Different waveband deep/shallow water region division thresholds and delineated optical deep water regions

a. Blue band division threshold; b. blue band to determine deep water area; c. green band division threshold; d. green band to determine deep water area

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图 9 卫星遥感反演水深有效区域内信息分布

Fig. 9 Information distribution in the effective area of satellite derived bathymetry

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图 10 边界区域统计信息

Fig. 10 Statistics information about the boundary area

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表 1 5种候选统计分布的概率密度函数及标准差求解公式

Tab. 1 Probability density functions and standard deviation computation formula of five candidate statistical distributions

分布函数	瑞利分布	韦伯尔分布	正态分布	伽马分布	对数正态分布
概率密度函数	${\dfrac{x}{v^2} {\rm{e}}^{-\frac{x^2}{2v^2} } }$	${\dfrac{\sigma}{\lambda}x^{v-1}{\rm{e}}^{\frac{x^v}{\lambda} } }$	${\dfrac{1}{\sqrt{2{\text{π}} v^2} }{\rm{e}}^{\frac{-(x-\lambda)^2}{2v^2} } }$	$\dfrac{\lambda^vx^{v-1} }{\varGamma(v)}{\rm{e}}^{-\lambda x}$	${\dfrac{1}{\sqrt{2{\text{π}}}xv}{\rm{e}}^{-\frac{({\rm{ln}}\;x-\lambda)^2}{2v^2} } }$
标准差	${\sqrt{4-{{\text{π}} }/2v} }$	${\lambda^{\frac{1}{v}} }$	1.482 6×MAD(x_i)	${\dfrac{1}{\lambda}\sqrt{v} }$	1.482 6×MAD(ln x_i)
注：MAD为中位数的绝对偏差。

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表 2 K-S检验的拟合优度检验结果

Tab. 2 Test results of the goodness of fit of K-S test

分布函数	瑞利分布	韦伯尔分布	正态分布	伽马分布	对数正态分布
蓝波段	0.32	0.11	0.08	0.06	0.04
绿波段	0.42	0.10	0.06	0.04	0.03
注：表中数据为最大偏差值，数值越小，拟合效果越好。

下载: 导出CSV

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