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<p>With the advancement of oblique photography technology, dense matching point clouds have found widespread application. As a key step in point cloud data processing, point cloud classification has gained continuous attention in 3D computer vision and spatial information processing. Addressing the challenges posed by the high-dimensional and redundant nature of point cloud data, which often leads to low classification accuracy, this study proposes a point cloud classification method based on feature selection and ensemble learning. First, we use a combination of Pearson correlation coefficient and random forest feature importance methods to screen point cloud data features, thereby eliminating redundant features to enhance the expression of critical features. Next, we construct a stacked ensemble model, KNN-SVM-RF-XG, using K-Nearest Neighbors, Support Vector Machine, Random Forest, and XGBoost as base models, with logistic regression as the meta-model. The experimental results demonstrated that the feature selection methods significantly enhanced the classification accuracy of point clouds, achieving peak accuracy of 96.03%. KNN-SVM-RF-XG model attained classification accuracy and precision of 96.20% and 96.19%, respectively, surpassing the performance of individual classifiers. Furthermore, the proposed model exhibited superior classification capabilities compared to deep learning approaches such as PointNet, PointNet++, and Transformer-based architectures, showcasing robust generalization ability. This research provides technical support for urban data renewal, land-use surveys, and urban planning management.</p>

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Scene point cloud classification based on stacked ensemble learning algorithm

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Dong Wu,
  • Weidong Yan,
  • Jingli Wang

摘要

With the advancement of oblique photography technology, dense matching point clouds have found widespread application. As a key step in point cloud data processing, point cloud classification has gained continuous attention in 3D computer vision and spatial information processing. Addressing the challenges posed by the high-dimensional and redundant nature of point cloud data, which often leads to low classification accuracy, this study proposes a point cloud classification method based on feature selection and ensemble learning. First, we use a combination of Pearson correlation coefficient and random forest feature importance methods to screen point cloud data features, thereby eliminating redundant features to enhance the expression of critical features. Next, we construct a stacked ensemble model, KNN-SVM-RF-XG, using K-Nearest Neighbors, Support Vector Machine, Random Forest, and XGBoost as base models, with logistic regression as the meta-model. The experimental results demonstrated that the feature selection methods significantly enhanced the classification accuracy of point clouds, achieving peak accuracy of 96.03%. KNN-SVM-RF-XG model attained classification accuracy and precision of 96.20% and 96.19%, respectively, surpassing the performance of individual classifiers. Furthermore, the proposed model exhibited superior classification capabilities compared to deep learning approaches such as PointNet, PointNet++, and Transformer-based architectures, showcasing robust generalization ability. This research provides technical support for urban data renewal, land-use surveys, and urban planning management.