ABSTRACT Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in the existing literature. But most generated features are based on geometric information of points. Several redundant and irrelevant features may not necessarily improve the filtering accuracy. Hence, this paper proposes a feature-selection method using minimal-Redundancy-Maximal-Relevance (mRMR) combined with Parzen window optimization to deal with both discrete and continuous features. An optimal/suboptimal feature subset is constructed for machine-learning filters in various landscapes. Experimental results based on AdaBoost show that height-related features, particularly height itself, are of the greatest significance in both urban and rural scenes. Moreover, different subsets can be selected from the datasets of the two landscapes by our feature-selection strategy, which increases the data relevance for describing each geographical landscape. This study provides guidelines for the selection of optimal/suboptimal features for point cloud filtering based on machine-learning algorithms.

中文翻译：

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机载 LiDAR 数据过滤的特征选择：一种具有 Parzon 窗口优化的互信息方法

摘要 滤波是从机载光探测和测距 (LiDAR) 数据生成数字高程模型 (DEM) 的关键步骤之一。近年来，基于机器学习的过滤器已成为一类过滤算法。由于点云拥有的可用特征有限，大多数现有研究主要集中在特征生成上。现有文献中已经描述了 30 多个特征。但大多数生成的特征都是基于点的几何信息。几个冗余和不相关的特征不一定能提高过滤精度。因此，本文提出了一种使用最小冗余最大相关（mRMR）结合 Parzen 窗口优化来处理离散和连续特征的特征选择方法。为各种景观中的机器学习过滤器构建了一个最优/次优特征子集。基于 AdaBoost 的实验结果表明，与高度相关的特征，尤其是高度本身，在城市和乡村场景中都具有最大的意义。此外，我们的特征选择策略可以从两个景观的数据集中选择不同的子集，这增加了描述每个地理景观的数据相关性。本研究为基于机器学习算法的点云过滤选择最优/次优特征提供了指导。通过我们的特征选择策略，可以从两个景观的数据集中选择不同的子集，这增加了描述每个地理景观的数据相关性。本研究为基于机器学习算法的点云过滤选择最优/次优特征提供了指导。通过我们的特征选择策略，可以从两个景观的数据集中选择不同的子集，这增加了描述每个地理景观的数据相关性。本研究为基于机器学习算法的点云过滤选择最优/次优特征提供了指导。