## 1 Introduction
However, the PointNet network simply connects all the points and only considers global features and individual point characteristics without local information, and the results of multi-instance, multi-classification problems are not good, which limits its ability to capture local structure and identify fine-grained models. [PERSON] et al. [20] proposed an improved PointNet++ architecture to address PointNet's limitations. While PointNet++ can extract point cloud features at multiple scales, it is still computationally intensive in multiscale neighborhoods due to its complex structure and architecture. In addition, like the original PointNet, PointNet++'s performance in semantic segmentation of complex language environments is unknown. In the current research on the semantic segmentation of point clouds, it is difficult to achieve fine classification by only relying on sparse three-dimensional coordinates of airborne point clouds. [PERSON] et al. [21] designed a refined feature extractor using a self-attention mechanism to improve the accuracy of point-cloud classification. [PERSON] et al. [22] proposed a graph attention feature fusion network (GAFFNet) that could achieve a satisfactory classification performance by capturing a wider range of contextual information of the ALS point cloud. [PERSON] et al. [23] confirmed the potential of using multispectral LiDAR in the classification of complex urban land cover through three comparison methods. Multispectral LiDAR has the advantage of detecting spectral information. [PERSON] et al. [24] constructed feature pyramids to integrate features at different scales and were able to classify point clouds with good results. However, these methods suffer from the problem of excessive consumption of memory or computational resources. In addition, building a deeper network structure is difficult; thus, the semantic information extraction remains insufficient. If the point cloud is directly used as the input in the PointNet network, the local feature extraction ability is slightly weak. Therefore, this paper improves the network based on PointNet, and proposes MSMF-PointNet (MSMF-PointNet: semantic segmentation of airborne LiDAR point clouds based on PointNet fusion with multiple scales and features). The proposed network uses PointNet as a classifier for urban terrain. However, the PointNet netalgorithm ignores the local characteristics of the problem; thus, on the basis of the multiscale spherical neighborhood calculation of airborne roughness of point clouds and the total variance, linearity, etc., the proposed method considers a variety of characteristics of covariance and combines remote sensing images, point-cloud fusion, XYZ coordinates, and RGB color images to produce a 16-dimensional feature vector as the input of the neural network. After multiple parameter combination experiments, the structure of PointNet is optimized to make up for the defects of the original network in generating classification results. This article is divided into five sections: introduction, materials and methods, experimental data, discussion, and conclusions. Section 1 introduces the research of some scholars and our research; Section 2 describes the structure and principle of the MSMF-PointNet model we use; Section 3 describes our experimental data and point-cloud classification results; Section 4 describes our experimental results and compares them to results obtained through other methods; Section 5 summarizes the experimental results and experimental analysis. From the final results, we can see that the method proposed in this paper can make full use of point-cloud features and improve the classification accuracy.