面向水流三维登记探索的多波束点云去噪及应用

针对多波束点云数据去噪难以保留精细特征，无法精确“锁定”河床形态问题，本文提出了一种面向自然资源确权水流三维登记探索的多波束点云去噪算法。以KD树搜索为基础，引入统计滤波理论进行多尺度噪声分类，并剔除大尺度噪声；针对小尺度噪声，在信息熵理论基础上，以主成分分析算法为基础，以信息熵最小原则确定最优邻域，并据此构建曲率信息熵对双边滤波因子进行优化改进，以实现水下地形点云去噪与精细特征保留的目的。试验结果表明，本文算法具有可行性，能够有效保证水下地形的精细特征，并能够应用于自然资源水流三维登记。

Multi-beam point cloud denoising and the application for 3D registration of water flow

Aiming at the problem that it is difficult to retain fine features and accurately "lock" riverbed morphology in multi-beam point cloud denoising, a multi-beam point cloud denoising algorithm is proposed for 3D registration of water flow of natural resources. On the basis of KD tree search, statistical filtering theory is introduced to classify multi-scale noise and eliminate large scale noise. Based on the principle of minimum information entropy, the optimal neighborhood is determined based on the principal component analysis (PCA) algorithm, and the curvature information entropy is constructed to optimize and improve the bilateral filtering factors, so as to achieve the purpose of denoising and preserving fine features of underwater terrain point cloud. Experiments show that the proposed algorithm is feasible, can effectively ensure the fine features of underwater terrain and applied to the 3D registration of water flow of natural resources.