Geographical object classification and information extraction is an important topic for the construction of 3D virtual reality and digital twin cities in urban areas. However, the majority of current multi-target classification of urban scenes uses only a single source data (optical remote sensing images or airborne laser scanning (ALS) point clouds), which is limited by the restricted information of the data source itself. In order to make full use of the information carried by multiple data sources, we often need to set more parameters as well as algorithmic steps. To address the above issues, we compared and analyzed the object classification methods based on data fusion of airborne LiDAR point clouds and optical remote sensing images, systematically. Firstly, the features were extracted and determined from airborne LiDAR point clouds and high-resolution optical images. Then, some key feature sets were selected and were composed of median absolute deviation of elevation, normalized elevation values, texture features, normal vectors, etc. The feature sets were fed into various classifiers, such as random forest (RF), decision tree (DT), and support vector machines (SVM). Thirdly, the suitable feature sets with appropriate dimensionality were composed, and the point clouds were classified into four categories, such as trees (Tr), houses and buildings (Ho), low-growing vegetation (Gr), and impervious surfaces (Is). Finally, the single data source and multiple data sources, the crucial feature sets and their roles, and the resultant accuracy of different classifier models were compared and analyzed. Under the conditions of different experimental regions, sampling proportion parameters and machine learning models, the results showed that: (1) the overall classification accuracy obtained by the feature-level data fusion method was 76.2% compared with the results of only a single data source, which could improve the overall classification accuracy by more than 2%; (2) the accuracy of the four classes in the urban scenes can reach 88.5% (Is), 76.7% (Gr), 87.2% (Tr), and 88.3% (Ho), respectively, while the overall classification accuracy can reach 87.6% with optimal sampling parameters and random forest classifiers; (3) the RF classifier outperforms DT and SVM for the same sample conditions. In this paper, the method based on ALS point clouds and image data fusion can accurately classify multiple targets in urban scenes, which can provide technical support for 3D scene reconstruction and digital twin cities in complex geospatial environments.

中文翻译：

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城市地区基于ALS和光学遥感图像的目标分类方法系统比较

地理对象分类与信息提取是城市地区3D虚拟现实和数字孪生城市建设的重要课题。然而，目前大多数城市场景的多目标分类仅使用单一源数据（光学遥感图像或机载激光扫描（ALS）点云），受到数据源本身信息的限制。为了充分利用多个数据源所承载的信息，我们往往需要设置更多的参数以及算法步骤。针对上述问题，我们对基于机载LiDAR点云与光学遥感影像数据融合的目标分类方法进行了系统的对比分析。首先，这些特征是从机载 LiDAR 点云和高分辨率光学图像中提取和确定的。然后，选择一些关键特征集，由高程的中值绝对偏差、归一化高程值、纹理特征、法线向量等组成。这些特征集被输入到各种分类器中，如随机森林（RF）、决策树（ DT) 和支持向量机 (SVM)。第三，组合合适维度的合适特征集，将点云分为树木（Tr）、房屋和建筑物（Ho）、低生植被（Gr）和不透水表面（Is）四类。 . 最后，比较分析了单数据源和多数据源、关键特征集及其作用，以及不同分类器模型的精度。在不同实验区域、采样比例参数和机器学习模型的条件下，结果表明：（1）与仅单一数据源的结果相比，特征级数据融合方法得到的整体分类准确率为76.2% ，可将整体分类准确率提高2%以上；（2）城市场景中四类的准确率分别可以达到88.5%（Is）、76.7%（Gr）、87.2%（Tr）和88.3%（Ho），而整体分类准确率可以达到87.6 % 具有最佳采样参数和随机森林分类器；(3) RF分类器在相同的样本条件下优于DT和SVM。本文基于ALS点云和图像数据融合的方法可以对城市场景中的多个目标进行准确分类，