改进局部稀疏系数的轻小型光子计数激光雷达去噪方法

栾奎峰; 张昆宁; 邱振戈; 王洁; 王振华; 薛烨; 朱卫东; 林丹丹; 赵雪燕

doi:10.11834/jrs.20221854

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改进局部稀疏系数的轻小型光子计数激光雷达去噪方法
Denoising method for light weight photon counting LiDAR based on an improved local sparse coefficient
2023年27卷第2期页码：520-532
纸质出版日期： 2023-02-07 ，
DOI： 10.11834/jrs.20221854

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栾奎峰，张昆宁，邱振戈，王洁，王振华，薛烨，朱卫东，林丹丹，赵雪燕.2023.改进局部稀疏系数的轻小型光子计数激光雷达去噪方法.遥感学报，27（2）： 520-532

Luan K F，Zhang K N，Qiu Z G，Wang J，Wang Z H，Xue Y，Zhu W D，Ling D D and Zhao X Y. 2023. Denoising method for light weight photon counting LiDAR based on an improved local sparse coefficient. National Remote Sensing Bulletin， 27（2）：520-532
栾奎峰，张昆宁，邱振戈，王洁，王振华，薛烨，朱卫东，林丹丹，赵雪燕.2023.改进局部稀疏系数的轻小型光子计数激光雷达去噪方法.遥感学报，27（2）： 520-532 DOI： 10.11834/jrs.20221854.

Luan K F，Zhang K N，Qiu Z G，Wang J，Wang Z H，Xue Y，Zhu W D，Ling D D and Zhao X Y. 2023. Denoising method for light weight photon counting LiDAR based on an improved local sparse coefficient. National Remote Sensing Bulletin， 27（2）：520-532 DOI： 10.11834/jrs.20221854.

摘要

无人机载光子计数激光雷达测深系统具有高探测灵敏度、高密度、小光斑的特点，是海岛礁和浅海水深快速测量的重要技术手段。然而，高探测灵敏度也导致了获取的光子点云数据具有背景噪声大、信噪比与地物类型的相关性强以及光子的密度分布差异大，已有的去噪算法不能很好地适用。本文提出一种原始光子观测数据的去噪方法，首先基于直方图统计的方法计算原始光子观测数据的有效信号区间，其次利用网格统计法对区间内数据进行粗去噪，最后改进局部稀疏系数方法，采用水平椭圆搜索计算格网内每个光子数据的局部稀疏系数值，基于最大类间方差法确定噪声光子和信号光子的分隔阈值，实现原始光子观测数据的精去噪。本文选取了海南省的加井岛及临近的浅海地形为研究区，获取了无人机载光子计数激光雷达光子数据，验证本文提出的去噪算法。结果表明：该方法在高信噪比海岛植被覆盖区域和砂质潮间带区域的F1-Score均值达94.64%和98.96%，在低信噪比的近岸较浅和较深水体区域的F1-Score均值达93.04%和90.74%，总体F1-Score为94.34%，能有效剔除绝大部分噪声点，且对不同信噪比的海岛植被、沙地和不同深度的水下地形具有较强的适应能力。此外，本文还选取南海地区的珊瑚岛星载ICESat-2光子数据集，初步验证了本文所提出去噪算法在星载光子点云数据上的可用性。

Abstract

The photon counting LiDAR bathymetry system carried by UAVs is an important method for island reef mapping and shallow water bathymetry due to the characteristics of high detection sensitivity and high density. However

the high detection sensitivity also leads to the acquired photonic point cloud data with large background noise

a strong correlation between the signal-to-noise ratio and the type of ground objects

and large differences in the density distribution of photons

and the existing denoising algorithms cannot be well applied.

In this paper

a denoising method for raw photon observation data is proposed. First

the effective signal interval of the raw photon observation data is calculated based on the histogram statistics method

and then the data in the interval are coarsely denoised by the grid statistics method. Finally

the local sparse coefficient method is improved

the horizontal ellipse search is used to calculate the local sparse coefficient value of each photon data in the grid

and the method of maximum interclass variance is introduced to determine the separation threshold of noise photons and signal photons

which improves the original photon observation data. Denoising accuracy. Jiajing Island and the adjacent shallow sea terrain in Hainan Province are selected as the research area to verify the denoising algorithm proposed.

The results show that the average F1-score in the high signal-to-noise ratio areas

such as the island vegetation coverage area and the sandy intertidal zone

reaches 94.64% and 98.96%

respectively

and the average F1-score in the low signal-to-noise ratio area

such as the shallower and deeper water bodies near the coast

can also reach 93.04% and 90.74%

respectively. The overall F1-score is 94.34%

which can effectively remove most of the noise points and has strong adaptability to island vegetation

sandy land and underwater terrain of different depths with different signal-to-noise ratios.

In addition

this paper also selects the spaceborne ICESat-2 photon dataset of coral islands in the South China Sea

which further verifies the availability and applicability of the denoising algorithm proposed in this paper on spaceborne photonic point cloud data.

关键词

遥感光子计数无人机激光雷达局部稀疏系数最大类间方差法去噪

Keywords

remote sensingphoton countingUAVLiDARlocal sparse coefficientOTSUnoise removal

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