【摘要】 星载光子计数激光雷达在接收信号的过程中会产生大量噪声，并且在复杂的山区地形中信噪比低，极大地影响对植被点云信号的准确提取。为解决该问题，提出了一种基于山地坡度的密度聚类算法。通过分析点云数据的密度和森林目标地形特征，用最大密度中心搜索法进行粗去噪，基于点云数据计算坡度角以优化密度聚类，完成数据精去噪。通过对提取的森林区域信号进行分类，拟合植被冠层廓线和地表廓线，结果表明本算法提取植被光子信号的准确率较高，地面与冠层廓线的RMSE分别为0.3588 m和3.7449 m，更适用于植被遥感点云数据处理。更多还原

【Abstract】 A large amount of noise will be generated while spaceborne photon counting LIDAR receive signals, and the signal-to-noise ratio is lower in complex mountainous land, which greatly affects the accurate extraction of vegetation point cloud signals. This paper proposes a density clustering algorithm based on the mountain slope to solve this problem. By analyzing the density of point cloud data and the terrain characteristics of forest targets, coarse noise removal is performed by using the maximum density center search method, and then the slope angle is calculated based on the point cloud data to optimize density clustering and complete the data fine noise removal. By classifying the extracted forest region signal, fitting the vegetation canopy profile and the surface profile, the results show that the proposed algorithm has high accuracy in the extraction of vegetation photon signal, and the RMSE of the ground and canopy profiles are 0. 3588 m and 3. 7449 m, respectively, which is more suitable for vegetation remote sensing point cloud data processing.更多还原