ABSTRACT

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) uses a 532 nm band photon-counting LiDAR (Light Detection and Ranging), which has certain penetrability to water bodies, and the measured data show that the bathymetric ability reaches nearly one Secchi depth. ATLAS has a limited number of beams and a fixed ground track, and only collects section elevation along the track direction. The fusion of active laser point cloud and passive optical remote sensing satellite image can fill the gap of shallow water depth data in a large range. This paper takes ATLAS as the research object, with the aim of exploring the effective algorithm flow of spaceborne active laser and passive optical fusion processing, and systematically evaluating the bathymetric accuracy of the fusion algorithm. An adaptive Gauss filtering technology based on the density of point cloud was firstly improved to achieve accurate denoising under the condition of uneven surface/underwater density. Subsequently, the depth of underwater points was calculated automatically through steps of water surface modelling, refraction correction, etc. Finally, the control points and check points were randomly extracted to solve the parameters of multispectral inversion model and verify the internal accuracy of the model. In this paper, the accuracy of ATLAS bathymetry was verified by Airborne LiDAR Bathymetry (ALB) data in Oahu Island, Hawaii, and the results indicate that the vertical root mean square error (RMSE) ranges from 0.56 m to 1.11 m. In Yongle islands and Qilianyu area of the South China Sea, WorldView-2 (WV2) 4 bands multispectral images and ATLAS data were used to carry out the active-passive fusion bathymetry, and the ALB and sonar data were used to evaluate the accuracy. Experimental results show that the internal compliance accuracy of the fusion model is better than 1.25 m (RMSE), and the real bathymetry accuracy is better than 1.42 m (RMSE). The above results reveal the great potential of active-passive fusion bathymetry based on ICESat-2 and other high-resolution remote sensing satellites, which can provide strong technical support for filling the blank of shallow water depth information.

摘要

ICESat-2（冰，云和陆地高程卫星2）上的高级地形激光测高仪系统（ATLAS）使用532 nm波段的光子计数LiDAR（光检测和测距），它对水体具有一定的渗透性，并且测量数据表明，测深能力达到了近一个塞基深度。ATLAS的光束数量有限，并且地面轨道固定，只能收集沿轨道方向的剖面高程。主动激光点云和被动光学遥感卫星图像的融合可以在较大范围内填补浅水深度数据的空白。本文以ATLAS为研究对象，旨在探索星载主动激光与无源光学融合处理的有效算法流程，并系统地评估该融合算法的测深精度。首先改进了基于点云密度的自适应高斯滤波技术，以在不均匀的地表/水下密度条件下实现精确的去噪。随后，通过水面建模，折射校正等步骤自动计算水下点的深度。最后，随机提取控制点和检查点，以解决多光谱反演模型的参数并验证模型的内部准确性。本文通过夏威夷瓦胡岛的机载激光雷达测深仪（ALB）数据验证了ATLAS测深仪的准确性，结果表明垂直均方根误差（RMSE）介于0.56 m至1.11 m之间。在南海的永乐群岛和祁连玉地区，使用WorldView-2（WV2）4波段多光谱图像和ATLAS数据进行主动-被动融合测深，并使用ALB和声纳数据评估准确性。实验结果表明，该融合模型的内部一致性精度优于1.25 m（RMSE），实际测深精度优于1.42 m（RMSE）。以上结果表明，基于ICESat-2等高分辨率遥感卫星的主动-被动融合测深技术具有巨大的潜力，可以为填补浅水深度信息的空白提供强有力的技术支持。实际测深精度优于1.42 m（RMSE）。以上结果表明，基于ICESat-2等高分辨率遥感卫星的主动-被动融合测深技术具有巨大的潜力，可以为填补浅水深度信息的空白提供强有力的技术支持。实际测深精度优于1.42 m（RMSE）。以上结果表明，基于ICESat-2等高分辨率遥感卫星的主动-被动融合测深技术具有巨大的潜力，可以为填补浅水深度信息的空白提供强有力的技术支持。