Semantic interpretation of the 3D scene is one of the most challenging problems in point cloud processing, which also deems as an essential task in a wide variety of point cloud applications. The core task of semantic interpretation is semantic labeling, namely, obtaining a unique semantic label for each point in the point cloud. Despite several reported approaches, semantic labeling continues to be a challenge owing to the complexity of scenes, objects of various scales, and the non-homogeneity of unevenly distributed points. In this paper, we propose a novel method for obtaining semantic labels of airborne laser scanning (ALS) point clouds involving the embedding of local context information for each point with multi-scale deep learning, nonlinear manifold learning for feature dimension reduction, and global graph-based optimization for refining the classification results. Specifically, we address the tasks of learning discriminative features and global labeling smoothing. The key contribution of our study is threefold. First, a hierarchical data augmentation strategy is applied to enhance the learning of deep features based on the PointNet++ network and simultaneously eliminate the artifacts caused by division and sampling while dealing with large-scale datasets. Subsequently, the learned hierarchical deep features are globally optimized and embedded into a low-dimensional space with a nonlinear manifold-based joint learning method with the removal of redundant and disturbing information. Finally, a graph-structured optimization based on the Markov random fields algorithm is performed to achieve global optimization of the initial classification results that are obtained using the embedded deep features by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. We conducted thorough experiments on ALS point cloud datasets to assess the performance of our framework. Results indicate that compared to other commonly used advanced classification methods, our method can achieve high classification accuracy. The overall accuracy (OA) of our approach on the ISPRS benchmark dataset can scale up to 83.2% for classifying nine semantic classes, thereby outperforming other compared point-based strategies. Additionally, we evaluated our framework on a selected portion of the AHN3 dataset, which provided OA up to 91.2%.

3D场景的语义解释是点云处理中最具挑战性的问题之一，这也被视为各种点云应用程序中的一项基本任务。语义解释的核心任务是语义标记，即为点云中的每个点获取唯一的语义标记。尽管已报道了几种方法，但是由于场景，各种规模的对象的复杂性以及不均匀分布点的不均一性，语义标记仍然是一个挑战。在本文中，我们提出了一种获取机载激光扫描（ALS）点云的语义标签的新方法，该方法包括使用多尺度深度学习，用于特征维数缩减的非线性流形学习，嵌入每个点的局部上下文信息，以及基于全局图的优化来细化分类结果。具体来说，我们解决了学习区分特征和全局标签平滑的任务。我们研究的关键贡献是三方面的。首先，应用分层数据增强策略来增强基于PointNet ++网络的深度特征的学习，并同时消除在处理大规模数据集时由除法和采样引起的伪像。随后，通过基于非线性流形的联合学习方法对学习到的分层深度特征进行全局优化，并将其嵌入到低维空间中，从而消除了冗余和令人不安的信息。最后，通过构造加权间接图并用图割来解决优化问题，基于马尔可夫随机场算法进行图结构优化，以实现对初始分类结果的全局优化，这些初始分类结果是使用嵌入的深层特征获得的。我们对ALS点云数据集进行了全面的实验，以评估我们框架的性能。结果表明，与其他常用的高级分类方法相比，我们的方法可以实现较高的分类精度。我们的方法在ISPRS基准数据集上的整体准确性（OA）可以扩展到83.2％，可以对9个语义类进行分类，从而胜过其他基于点的比较策略。此外，我们在AHN3数据集的选定部分上评估了我们的框架，该部分提供了高达91的OA。