摘要
针对当前无输入参数的四叉树去噪方法在强噪声背景下对噪声光子误识率较高的缺陷,提出了一种无输入参数的适应强噪声背景的ICESat-2点云去噪方法。首先,以剪枝四叉树的层值表征光子密度,避免强噪声背景下局部稀疏、相距较近的噪声光子被四叉树分隔层次较多而被误表征为密度过大;然后,自适应求取信号光子的密度阈值,完成噪声光子的一级去除;最后,通过箱线图进一步去除少量未能被剪枝四叉树识别的局部较密的离群噪声光子,实现去噪过程。选择美国北达科他州和加利福尼亚州两个研究区域,对强噪声背景下的ICESat-2点云开展去噪实验验证。实验结果表明:与四叉树去噪方法相比,本文所提方法明显具有更优的去噪性能;本文所提方法去噪后所得信号光子拟合的冠顶和地表曲线与分辨率为1 m的机载激光雷达高程数据产品剖面高程曲线基本一致。
The Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)was launched on 15 September2018 to measure ice sheet and glacier elevation change,land elevation,global vegetation elevation and monitor clouds and aerosols.The sole instrument on-board ICESat-2 is the Advanced Topographic Laser Altimeter System(ATLAS).ATLAS employs a micro-pules multi-beam photon-counting laser lidar technology,which is the first time this technology has been applied to a spaceborne platform.However,since the laser pulses emitted and detected by ATLAS are weak signals,the ICESat-2 data introduces a significant number of noise photons.The denoising of the ICESat-2 data is a key point for its application.A few algorithms have been proposed to remove noise photons in the ICESat-2 data,which are based on the criterion that signal photons are more densely distributed than noise photons.Most of the denoising methods nowadays depend on the set parameters and the parameter-free method is becoming a new frontier.To fix the current parameter-free quadtree denoising method which misidentifies noise photons under the strong noise background,this paper proposes an improved parameter-free denoising method for the ICESat-2 point cloud.For avoiding the noise photons sparse in density but close in the distance in a partial area,which means photons may be separated by the original quadtree and misrepresented as a high density,the pruned quadtree is used to represent a suitable density.According to the location of ICESat-2photons,the initial space is given and recursively divided into four quadrants.Instead of dividing until each quadrant contains no more than one photon,a quadrant is not divided in the case that the quadrant is divided once and its internal photons are not separated.The density of photon is the corresponding level value in the tree structure.Then,several equidistant windows are divided according to the along track distance to adapt the changes of SNR.The Otsu method adaptively calculates the photon density threshold of each window.Photons wit
作者
刘翔
张立华
戴泽源
陈秋
周寅飞
LIU Xiang;ZHANG Lihua;DAI Zeyuan;CHEN Qiu;ZHOU Yinfei(Department of Military Oceanography and Hydrography&Cartography,Dalian Naval Academy,Liaoning 116018,China;Key Laboratory of Hydrographic Surveying and Mapping of PLA,Dalian Naval Academy,Liaoning 116018,China;91001 Troops,Beijing 100071,China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2022年第11期346-356,共11页
Acta Photonica Sinica
基金
国家自然科学基金(Nos.41871369,41901320,42071439,41871295)。
