主被动光学卫星遥感数据融合浅海水深测量

doi:10.13474/j.cnki.11-2246.2023.0305

测绘通报 ›› 2023, Vol. 0 ›› Issue (10): 117-122.doi: 10.13474/j.cnki.11-2246.2023.0305

主被动光学卫星遥感数据融合浅海水深测量

张春玲¹, 李由之², 张刚^3,4, 邱振戈⁵

1. 河南省地图院, 河南郑州 450008;
2. 南京大学地理与海洋科学学院, 江苏南京 210023;
3. 北京四维远见信息技术有限公司, 北京 100070;
4. 中国测绘科学研究院, 北京 100830;
5. 上海海洋大学海洋科学学院, 上海 201306

收稿日期:2023-01-28 发布日期:2023-10-28
作者简介:张春玲(1964-),女,高级工程师,主要研究方向为摄影测量与遥感。E-mail:zhangclrs@163.com

Shallow water depth measurement based on fusion of active and passive optical satellite remote sensing data

ZHANG Chunling¹, LI Youzhi², ZHANG Gang^3,4, QIU Zhenge⁵

1. Map Institute of Henan Province, Zhengzhou 450008, China;
2. School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;
3. Beijing Geo-Vision Technology Limited Company, Beijing 100070, China;
4. Chinese Academy of Surveying and Mapping, Beijing 100830, China;
5. College of Marine Science, Shanghai Ocean University, Shanghai 201306, China

Received:2023-01-28 Published:2023-10-28

摘要/Abstract

摘要： 针对双介质立体摄影测量中水面高程不确定性带来的测深误差,本文提出了主被动光学卫星遥感数据融合浅海水深测量的技术路线,探讨了主动ICESat-2激光测高数据与被动WorldView-3立体像对数据融合的测深模型。首先通过ICESat-2 激光测高数据提取海面高程作为WorldView-3立体像对数据水面高程纠正的起算位置,然后利用区域网平差的二次多项式加入适当的水深改正量。在我国南海岛礁开展了主被动光学卫星遥感数据融合进行浅海水深测量试验。结果表明,主被动融合测深模型的均方根误差(RMSE)小于0.70 m,实现了高精度、无实测控制点的浅海水深测量。

关键词: 浅海测深, 主被动融合, ICESat-2, WorldView-3, 光学卫星遥感

Abstract: Aiming at the bathymetric error caused by the uncertainty of water surface elevation in dual-medium stereo photogrammetry, a technical route of shallow water depth measurement based on fusion of active and passive optical satellite remote sensing data is proposed, and the bathymetric model of integrating active ICESat-2 laser altimetry data and passive WorldView-3 stereo image pair data is studied. Firstly, the sea surface elevation is extracted from ICESat-2 laser altimetry data as the starting position for water surface elevation correction of WorldView-3 stereo image pair data. Then the appropriate water depth correction is added using the quadratic polynomial of the area network parity. A shallow water bathymetry experiment is carried out on the fusion of active and passive optical satellite remote sensing data in South China Sea. The results show that the root mean square error (RMSE) of the active and passive fusion bathymetry model is less than 0.70 m. The active-passive fusion bathymetric model realizes high-precision shallow water bathymetry without measured control points.

Key words: shallow water bathymetry, active-passive fusion, ICESat-2, WorldView-3, optical satellite remote sensing

中图分类号:

P237

张春玲, 李由之, 张刚, 邱振戈. 主被动光学卫星遥感数据融合浅海水深测量[J]. 测绘通报, 2023, 0(10): 117-122.

ZHANG Chunling, LI Youzhi, ZHANG Gang, QIU Zhenge. Shallow water depth measurement based on fusion of active and passive optical satellite remote sensing data[J]. Bulletin of Surveying and Mapping, 2023, 0(10): 117-122.

参考文献

[1] 曹彬才.遥感测深数据处理方法研究[D].郑州: 信息工程大学, 2017.
[2] CAO Bincai,FANG Yong,JIANG Zhengzhi,et al.Shallow water bathymetry from WorldView-2 stereo imagery using two-media photogrammetry[J].European Journal of Remote Sensing,2019,52(1): 506-521.
[3] HOCHBERG E J,ANDREFOUET S,TYLER M R.Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in near-shore environments[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(7): 1724-1729.
[4] PIKE S,TRAGANOS D,POURSANIDIS D,et al.Leveraging commercial high-resolution multispectral satellite and multibeam sonar data to estimate bathymetry: the case study of the Caribbean Sea[J].Remote Sensing,2019,11(15): 1830.
[5] CAHALANE C,MAGEE A,MONTEYS X,et al.A comparison of Landsat 8,RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry[J].Remote Sensing of Environment,2019,233: 111414.
[6] CASAL G,MONTEYS X,HEDLEY J,et al.Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data[J].International Journal of Remote Sensing,2019,40(8): 2855-2879.
[7] SCHUTZ B E,ZWALLY H J,SHUMAN C A,et al.Overview of the ICESat mission[J].Geophysical Research Letters,2005,32(21): L21S01.
[8] MARKUS T,NEUMANN T,MARTINO A,et al.The Ice,Cloud,and land Elevation Satellite-2(ICESat-2): science requirements,concept,and implementation[J].Remote Sensing of Environment,2017,190: 260-273.
[9] 黄庚华,童鹏,丁宇星,等.星载激光测高仪高精度波形数字化获取技术研究[J].航天返回与遥感,2020,41(2): 39-46.
[10] 曹彬才,邱振戈,朱述龙,等.高分辨率卫星立体双介质浅水水深测量方法[J].测绘学报,2016,45(8): 952-963.
[11] 曹斌,朱述龙,邱振戈,等.WorldView-2影像双介质摄影测量的浅海地形测绘试验[J].遥感学报,2018,22(5): 745-757.
[12] PARRISH C E,MAGRUDER L A,NEUENSCHWANDER A L,et al.Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS's bathymetric mapping performance[J].Remote Sensing,2019,11(14): 1634.
[13] CHEN Yifu,LE Yuan,ZHANG Dongfang,et al.A photon-counting LiDAR bathymetric method based on adaptive variable ellipse filtering[J].Remote Sensing of Environment,2021,256: 112326.
[14] 李铜基.中国近海海洋: 海洋光学特性与遥感[M].北京: 海洋出版社,2012.
[15] 曹彬才,邱振戈,朱述龙,等.双介质摄影测量的折射改正方法[J].哈尔滨工程大学学报,2016,37(11): 1479-1484.

主被动光学卫星遥感数据融合浅海水深测量

Shallow water depth measurement based on fusion of active and passive optical satellite remote sensing data

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	刘冬枝, 任毅, 朱万雄. 基于WorldView-3卫星带状影像生产地形级地理场景数据[J]. 测绘通报, 2023, 0(9): 135-138.
[2]	钟静, 孙杰, 赖祖龙, 谌一夫. ICESat-2与Sentinel-2数据融合的深度学习浅滩水深测量[J]. 测绘通报, 2023, 0(7): 39-43.
[3]	王子赫, 康彦彦, 王敏京. ICESat-2与多源光学影像的潮滩地形反演方法[J]. 测绘通报, 2023, 0(5): 51-55,61.
[4]	刘卓, 李佳, 张翔, 郭磊, 刘艳阳, 徐浩栋, 顾云杨, 陈重华. 利用TanDEM-X影像和ICESat-2高程数据获取南极高精度数字高程模型[J]. 测绘通报, 2022, 0(4): 72-76.
[5]	丁夏萌, 张继贤, 郭婧, 张鹤, 常亚茹. 基于ICESat-2 ATLAS数据的DEM高程精度评价[J]. 测绘通报, 2022, 0(12): 84-90.
[6]	王楠, 汪桂生, 张震. 高分遥感影像下的大通废弃煤矿区生态修复林淹水胁迫灾情信息提取[J]. 测绘通报, 2018, 0(7): 13-17,47.