Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

中文翻译：

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用于森林环境中机载 LiDAR 地面点滤波的基于点的全卷积神经网络

机载激光扫描 (ALS) 数据是生成地形产品最常用的数据之一。过滤地面点是 ALS 数据处理的先决步骤。传统的过滤方法主要使用手工特征或预定义的分类规则和预处理/后处理操作来迭代过滤地面点，这是经验性的和繁琐的。深度学习提供了一种解决分类和分割问题的新方法，因为它具有自学习特征的能力，这在许多领域，尤其是遥感领域受到青睐。在本文中，我们提出了一种基于点的全卷积神经网络 (PFCN)，它直接使用仅具有几何信息的点，并提取逐点和逐块特征以对每个点进行分类。该网络使用来自 14 个站点的 37449157 个点进行训练，并在各种森林环境中的 6 个站点上进行评估。此外，将该方法与五种广泛使用的过滤方法和最好的基于点的深度学习方法之一（PointNet++）进行了比较。结果表明，PFCN 在平均遗漏误差（T1 = 1.10%）、总误差（Te = 1.73%）和 Kappa 系数（93.88%）方面取得了最好的结果，但在均方根误差方面排名第二。数字地形模型造成的最坏佣金错误。此外，我们的方法在准确性上与 PointNet++ 相当甚至更好。此外，该方法消耗了三分之一的计算资源和七分之一的训练时间。我们认为 PFCN 是一种简单灵活的方法，可以广泛应用于地面点滤波。