| ICESat-2 ATL08去噪算法实现及精度评价 |
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| 引用本文： | 曹彬才,方勇,江振治,高力,胡海彦.ICESat-2 ATL08去噪算法实现及精度评价[J].测绘通报,2020,0(5):25-30. |
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| 作者姓名： | 曹彬才 方勇 江振治 高力 胡海彦 |
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| 作者单位： | 1. 地理信息工程国家重点实验室, 陕西 西安 710054;2. 西安测绘研究所, 陕西 西安 710054 |
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| 基金项目： | 高分辨率对地观测重大专项（GFZX01010202；GFZX04031204） |
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| 摘 要： | 针对光子计数激光点云特点,研究实现了ICESat-2卫星ALT08技术文档点云去噪算法与精度评估。首先逐点计算目标点邻域范围内点的总数作为局部密度值,由于信号通常比噪声在空间分布上更集中,密度直方图常呈噪声在左、信号在右的双峰特点;然后通过峰值计算、距离判断等计算噪声与信号高斯函数初值,并采用高斯函数加期望方差最大算法拟合两个精确的波形位置;最后将双高斯函数交点位置作为去噪阈值,小于阈值的点标记为噪声,大于阈值的为信号。通过MABEL数据的试验结果表明:当信号噪声呈现典型双峰分布特征时,算法去噪效果优秀,精度优于98%,处理不符合双峰特征数据时效果变差。
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| 关 键 词： | 光子计数 激光雷达 空间密度 去噪算法 精度评价 |
| 收稿时间： | 2019-09-03 |
Implementation and accuracy evaluation of ICESat-2 ATL08 denoising algorithms |
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| CAO Bincai,FANG Yong,JIANG Zhenzhi,GAO Li,HU Haiyan.Implementation and accuracy evaluation of ICESat-2 ATL08 denoising algorithms[J].Bulletin of Surveying and Mapping,2020,0(5):25-30. |
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| Authors: | CAO Bincai FANG Yong JIANG Zhenzhi GAO Li HU Haiyan |
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| Institution: | 1. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China;2. Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China |
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| Abstract: | According to the characteristics of photon counting laser point cloud, this paper studies and implements the point cloud denoising algorithm in ICESat-2 ATL08 technical document and carries out the accuracy evaluation. Firstly, the total number of points in the neighborhood of the target point is calculated point by point as the local density value. Since the signal is usually more concentrated than the noise in the spatial distribution, the density histogram often exhibits the double peak characteristic of noise on the left and the signal on the right. Then, initial parameters of noise and signal Gaussian function are calculated by peak calculation and distance judgment, and Gaussian fit and expectation-maximization algorithm are adopted for fitting two exact waveform position. Finally, the intersection position of the double Gaussian function is taken as the denoising threshold. The points smaller than the threshold are marked as noise, and the points greater than the threshold are signals. The experimental results of MABEL data show that when the data exhibits typical bimodal distribution characteristics, the algorithm has excellent denoising effect, and the accuracy is better than 98%. When the data does not meet the bimodal characteristics, the effect becomes worse. |
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| Keywords: | photon counting LiDAR spatial density denoising algorithm accuracy evaluation |
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