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基于海底回波增强的机载LiDAR测深水体旅行时提取算法

张一衡 于孝林 亓超 宿殿鹏 王志良 任国贞

张一衡,于孝林,亓超,等. 基于海底回波增强的机载LiDAR测深水体旅行时提取算法[J]. 海洋学报,2023,45(12):145–155 doi: 10.12284/hyxb2023167
引用本文: 张一衡,于孝林,亓超,等. 基于海底回波增强的机载LiDAR测深水体旅行时提取算法[J]. 海洋学报,2023,45(12):145–155 doi: 10.12284/hyxb2023167
Zhang Yiheng,Yu Xiaolin,Qi Chao, et al. An algorithm for extracting airborne LiDAR bathymetric travel time in water column based on seabed echo enhancement[J]. Haiyang Xuebao,2023, 45(12):145–155 doi: 10.12284/hyxb2023167
Citation: Zhang Yiheng,Yu Xiaolin,Qi Chao, et al. An algorithm for extracting airborne LiDAR bathymetric travel time in water column based on seabed echo enhancement[J]. Haiyang Xuebao,2023, 45(12):145–155 doi: 10.12284/hyxb2023167

基于海底回波增强的机载LiDAR测深水体旅行时提取算法

doi: 10.12284/hyxb2023167
基金项目: 自然资源部海洋环境探测技术与应用重点实验室开放基金项目(MESTA-2020-B004);青岛市关键技术攻关及产业化示范类项目(23-1-3-hygg-1-hy);国家自然科学基金项目(41930535,52001189,42304051); 山东省高等学校青创科技支持计划项目(2023KJ088);自然资源部渤海生态预警与保护修复重点实验室开放基金项目(2023107);中国博士后科学基金项目(2021M700155);山东科技大学科研创新团队支持计划项目(2019TDJH103)。
详细信息
    作者简介:

    张一衡(2000—),男,山东省临沂市人,主要从事机载LiDAR测深数据处理与应用方面研究。E-mail:zhangyiheng@sdust.edu.cn

    通讯作者:

    宿殿鹏(1988—),男,山东省莱州市人,副教授,主要从事机载LiDAR测深数据处理与应用方面研究。E-mail: sudianpeng@sdust.edu.cn

  • 中图分类号: P714+.6

An algorithm for extracting airborne LiDAR bathymetric travel time in water column based on seabed echo enhancement

  • 摘要: 机载LiDAR测深(Airborne LiDAR Bathymetry, ALB)技术具有高精度、高效率、强机动性、水陆两用等优势,特别适合海岸带、海岛礁等浅水海域复杂地形的快速探测。激光穿透水体时能量将迅速衰减,导致部分海底回波难以有效提取,海底真实位置判别困难。为此,本文提出一种基于回波增强的机载LiDAR测深水体旅行时提取算法。通过Gold去卷积算法来恢复目标横截面形状,确定海底初始回波范围;随后采用双指数函数拟合水体后向散射有效范围,进而求取波形漫衰减系数Kd值;最后结合海底激光雷达方程,利用Kd值对海底初始回波范围内波形进行增强,并利用高斯函数分解增强后回波,确定海底位置参数,从而实现ALB波形的水体旅行时提取。利用青岛胶州湾RIEGL VQ-840-G ALB实验数据对本文算法的可行性进行验证,将本文算法与理查德森-露西(Richardson-Lucy,RL)去卷积模型、峰值探测模型进行了比对,结果表明本文算法与单波束同名点之间高程误差的均方根误差(Root Mean Square Error, RMSE)为18.5 cm,较上述两种算法分别降低了29.9%、41.4%。因此,本文算法具有可行性,能够满足ALB波形的水体旅行时高精度提取,可为机载LiDAR测深数据精细化处理提供一定技术支撑。
  • 图  1  目标横截面恢复过程示意图

    Fig.  1  Schematic diagram of the restoration process of target cross section

    图  2  海底初始回波范围选择

    Fig.  2  Selection of initial seabed echo range

    图  3  水体后向散射波形求解Kd

    Fig.  3  Kd value is solved by backscattered waveforms of water bodies

    图  4  高斯分解增强波形

    Fig.  4  Gaussian decomposition enhanced waveform

    图  5  本文算法流程图

    Fig.  5  Flowchart of the algorithm in this paper

    图  6  无人机载LiDAR测深实验概况

    Fig.  6  General situation of UAV-borne LiDAR bathymetry experiment

    图  7  3类典型波形的目标横截面恢复结果

    a1–a3为极浅水波形、常规波形、微弱波形;b1–b3为Gold去卷积所恢复对应目标横截面;c1–c3为RL去卷积所恢复对应目标横截面

    Fig.  7  Target cross section restoration results of three types of typical waveforms

    a1–a3. Extremely shallow water waveform, conventional waveform, weak waveform; b1–b3. Gold deconvolution recovered corresponding target cross section; c1–c3. RL deconvolution restores corresponding target cross sections

    图  8  横截面结果所解水下斜距范围

    Fig.  8  Range of underwater oblique distance solved by cross section results

    图  9  典型波形目标横截面恢复结果

    Fig.  9  Restoration results of cross-section of typical waveform targets

    图  10  3种算法处理单条带效果

    Fig.  10  Renderings of single strip processed by three algorithms

    图  11  3种算法局部展示点云图

    Fig.  11  Three algorithms partially display point cloud images

    图  12  3种算法与单波束同名点的误差值

    Fig.  12  Error values of the three algorithms with the same name as single beam points

    表  1  RIEGL VQ-840-G无人机载LiDAR测深系统主要技术参数指标

    Tab.  1  Main technical parameters of RIEGL VQ-840-G UAV-borne LiDAR bathymetry system

    参数 指标
    扫描频率 200 kHz(可调节)
    最大穿透深度 2.5 Secchi @ 50 kHz
    视场角 40°
    测点密度 > 100 pts/m2
    光斑 10 cm @ 100 m航高
    重量 12 kg
    下载: 导出CSV

    表  2  3种算法所生成点云的点密度统计

    Tab.  2  Point density statistics of point clouds generated by the three algorithms

    算法 平均密度/(point·m−2
    本文算法 271
    RL去卷积 192
    峰值探测 185
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-17
  • 修回日期:  2023-10-07
  • 网络出版日期:  2023-12-29
  • 刊出日期:  2023-12-01

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