## 1 Introduction
The 3 DSC is simple in construction, strong in discrimination and insensitive to noise, but it is time-consuming. The Unique Shape Context (USC) descriptor [14] improves the 3 DSC to avoid ambiguities in the classification. Point Feature Histograms (PFH) [15] are local features, which construct a feature histogram with the angles and distances of the normal vectors of any two points in the specified point neighborhood. The descriptor can accurately describe the local features of the points, but the computation is large and the real-time performance is poor. Fast Point Feature Histograms (FPFH) [16] are a simplification of PFH, which greatly reduce the time consumption while retaining most of the description performance of PFH. FPFH have an excellent performance, and are widely used in the field of point cloud classification, segmentation and registration [17]. Although these features can express the local features of the point cloud, they do not take into account the characteristics of the ALS point cloud, which has the characteristics of relative sparse, rich elevation information, as well as a horizontal and vertical distribution. (3) Single point classification based on features and classifiers. Currently, machine learning is an important method for classification problems. The single-point classification based on machine learning takes the feature vector of the point as the input and the class label of the single point as the output. Common machine learning algorithms can accomplish this classification task, such as AdaBoost [18], Random Forest [19] and Support Vector Machine (SVM) [20]. This kind of method uses a classifier to learn the local features of each point, after which the parameters in the classifier are determined based on the training dataset. Finally, the test set is classified by the classifier. This classification strategy can more accurately segment the boundary regions between different adjacent objects, and this method has a better performance in detail. However, due to the extremely large number of points, the calculation amount is large. Thus, the model training is slow, and there are also some misclassifications of local regions. However, there are always some errors in the final classification results. Therefore, the initial classification results are required to further optimize according to the characteristics of the point cloud. In order to solve the above-mentioned problems, an ALS point cloud classification algorithm based on a single point multi-feature fusion is proposed. This kind of algorithm is based on the point as the basic processing unit, and the classification process assigns labels to each point in the point cloud to realize a point cloud classification. The proposed method extracts the local features of each point by constructing a multi-scale neighborhood space, along with two new features: a normal angle distribution histogram (NAD) and latitude sampling histogram (LSH) are proposed. Following this, SVM is used for training and classification. However, since each point is classified, there is a problem regarding some edge points being misclassified. In this regard, the initial classification results are further optimized according to the neighborhood classification optimization of multi-scale pyramids. Experiments prove that the classification algorithm has a higher accuracy. The main contributions of this paper are as follows:  
(1) Two local features are proposed, that is, the NAD histogram and the LSH histogram. The differences of different objects in the normal distribution, and the difference of the neighborhood points around different objects in the horizontal and vertical directions of the three-dimensional space, can be fully utilized to more effectively represent the characteristics of different objects. (2) A multilevel single-point features fusion method based on a multi-neighborhood space and multi-resolution is proposed. The multi-scale space is constructed by changing the resolution of the point cloud and the number of the neighborhood.