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ANOVA guided high-order Markov network and its application in building extraction from point clouds

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

  • Email：20201248046@nuist.edu.cn
  • Introduction：郝娇娇，研究方向为点云数据智能处理。E-mail： 20201248046@nuist.edu.cn
HAO Jiaojiao,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

  • Email：nih@nuist.edu.cn
  • Introduction：倪欢，研究方向为遥感模式识别。E-mail： nih@nuist.edu.cn
NI Huan*,  
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

GUAN Haiyan

résumé

Building extraction is important for urban planning, land management, three-dimensional (3D) reconstruction, and other fields. Among various remote sensing techniques, 3D point cloud acquisition technique represented by Airborne Laser Scanning (ALS) system provides an efficient and convenient way for building extraction and modeling. Currently, deep learning-based building extraction methods are widely used. Compared with them, unsupervised building extraction methods do not need to train with a large amount of manually labeled data and do not require powerful computation equipment. Thus, developing unsupervised building extraction methods that do not rely on manual labeling is greatly meaningful.In this paper, based on the supervoxels of ALS point clouds, a high-order Markov network guided by the analysis of variance (ANOVA) is proposed for building extraction. This method first uses supervoxels as the nodes of the undirected graph, and then constructs high-order factors based on the principle of ANOVA and the local geometric features of 3D neighborhood. After that, the features are transformed into node and edge potential functions with a better expression ability. Finally, the belief propagation algorithm is used to make inference on the high-order Markov network, and an unsupervised building extraction framework is constructed.In the experiments, two groups of ALS point cloud datasets with ground-truths are employed, and four commonly used metrics are utilized to evaluate the accuracy of the results. Visual analysis shows that our method extracts buildings with complete interiors and clear boundaries. Hence, this method can be used to provide reliable data for the 3D reconstruction of buildings. According to quantitative analysis, in residential areas dominated by low-rise buildings, the averaged accuracy (the projection-area-based and object-based F1 indexes) of our method reaches 95.4% and 91.5% which are higher than that of existing supervised and unsupervised methods. In downtown areas dominated by high-rise buildings, the averaged object-based F1 score of our method reaches 93.5% which is higher than that of existing methods; and its averaged projection-area-based F1 score gets 92.9%, which is more than sufficient.This paper includes three innovative points. Firstly, an unsupervised building extraction framework from coarse to fine is constructed. Specifically, the Bayesian Gaussian Mixture Model is first used to capture the initial state of the node under the independent assumption, and then the high-order Markov network is designed to model the correlation of the 3D neighborhood for extracting buildings. Secondly, a potential function calculation method based on P-value test of ANOVA is constructed, which enhances the expression of interaction among supervoxels. Thirdly, higher-order factors are introduced into the supervoxel Markov network model, and the belief propagation algorithm is used for the inference of the network, thus the accurate identification of buildings is achieved. The results show that our method can effectively extract ALS point cloud buildings in different study areas, and the ablation study validates the positive effect of each module.

mots-clés

ALS;Gaussian Mixture Model;building extraction;ANOVA;high-order Markov network

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