ICESat-2(Ice,Cloud,and Land Elevation Satellite-2)激光卫星作为当前最先进的激光测高卫星之一,通过发射 532 nm波长的激光,能够有效获取浅海区域的水深数据,极大地推进了浅海测深技术的发展.然而,ICESat-2的原始数据常受到噪声点云的干扰,给数据的后期处理带来了不小的挑战.为提高数据处理的准确性和效率,本研究针对ICESat-2点云在水平方向上比垂直方向更为密集的特性,开发了一种基于多层感知机(Multilayer Perceptron,MLP)的去噪算法.该算法综合考虑了水平椭圆搜索区域内的点密度、点与点之间的平均距离、最近邻点间的距离(分别为 3和 5)等特征值,实现对噪声点的有效识别和去除.通过选取澳大利亚某岛礁区域的ICESat-2数据作为训练集,同时使用经过我国西沙群岛玉琢礁和东岛的数据对所提出的去噪模型进行验证.实验结果表明,本研究所提出的去噪方法正确率达到 90%以上,显著优于现有的OPTICS去噪算法以及基于置信度的去噪结果.这一成果不仅为ICESat-2数据的噪声去除提供了一种新的解决方案,也为相关领域的研究提供了可靠的数据支持.
A Machine Learning Based Point Cloud Denoising Algorithm for ICESat-2 Laser Satellite
ICESat-2(Ice,Cloud,and Land Elevation Satellite-2)is one of the most advanced laser altimetry satellites currently available.Using a laser wavelength of 532 nm,it can effectively acquire bathymetric data in the shallow water areas,thus greatly promoting the development of shallow water bathymetry.However,the raw data from ICESat-2 are often clouded by noisy point clouds so that great challenges can be brought to the post-processing of the data.To improve the accuracy and efficiency of data processing,a denoising algorithm based on Multilayer Perceptron(MLP)is developed to address the feature that ICESat-2 point clouds are denser in horizontal direction than in vertical direction.This algorithm takes into account comprehensively the feature values such as point density,average distance between points,and distance between the nearest neighbors(respectively 3 and 5)within a horizontal elliptical search area,thus realizing the effective identification and removal of noise points.The developed denoising model has been validated by selecting the ICESat-2 data from an reef island region in Australia as the training set and also using the data from the Jade Bangle Reef and the East Island in the Xisha Islands of China.The experimental results show that the denoising method developed in the study has an accuracy rate of over 90%,which is significantly better than the existing OPTICS denoising algorithm and the denoising results extracted by the official ATL03 algorithm.This achievement not only provides a new solution for the noise removal from ICESat-2 data,but also provides reliable data support for the research in related fields.
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