A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

Li, Yong; Tong, Guofeng; Du, Xiance; Xiang, Yang; Zhang, Jianjun; Yang, Lin

doi:10.3390/app9050951

Cited by 17 publications

(28 citation statements)

References 26 publications

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“…For the ALS point clouds, the elevation is one of the discriminative features for object recognition, thus the high-based descriptor F z is used. We use the feature F lsh [7] for cluster labeling because it has good discriminative ability. Specifically, to calculate the feature F lsh , we first select a point from a set of points as the center of a sphere, and derive a histogram of the neighborhood points in the latitudinal direction.…”

Section: Initial Coarse Labeling Of Fine-grained Clustersmentioning

confidence: 99%

“…This feature is used to distinguish different classes of points according to the distribution of the neighborhood points in the latitudinal direction. This feature has the advantages of anti-occlusion, no influence of the local coordinate system, as well as high efficiency [7]. The features used in this study are listed in Table 2.…”

Section: Initial Coarse Labeling Of Fine-grained Clustersmentioning

confidence: 99%

“…Those classification methods can be generally divided into two categories, namely the single point-based method and the cluster-based method. Generally, the single point-based method consists of neighborhood selection, feature extraction and selection, and classification of each individual point cloud [7][8][9][10]. A series of publications on this topic have demonstrated the effectiveness of the approach.…”

Section: Introductionmentioning

confidence: 99%

“…A series of publications on this topic have demonstrated the effectiveness of the approach. For example, Li et al [7] proposed an ALS point cloud classification method based on multilevel features fusion and pyramid neighborhood optimization. Weinmann et al [9] established a point cloud classification framework based on individual point clouds to obtain optimal features and achieve accurate classification results.…”

Section: Introductionmentioning

confidence: 99%

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Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

Remote Sensing

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This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy.

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Section: Initial Coarse Labeling Of Fine-grained Clustersmentioning

confidence: 99%

Section: Initial Coarse Labeling Of Fine-grained Clustersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

Remote Sensing

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“…Li et al [12] in their paper on "A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification" proposed (i) two local features including the normal angle distribution (NAD) histogram and latitude sampling histogram (LSH), (ii) a multilevel single-point features fusion method based on a multi-neighborhood space and multi-resolution, and (iii) a fast classification optimization method based on a multi-scale pyramid. They validated the proposed method using large-scale airborne laser scanning (ALS) point clouds.…”

Section: Machine Learning Techniques and Their Applicationsmentioning

confidence: 99%

Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Jung

Lee

2019

Applied Sciences

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A Feature Extraction Algorithm Based on Optimal Neighborhood Size

Jia

Pang

Xie

2022

Circuits Syst Signal Process

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The feature extraction of a point cloud fragment model is the basis of fragment splicing, which provides the technical support for research on the segmentation, splicing, and restoration of fragment surfaces. High-quality feature extraction, however, is a complicated process due to the diversity of the surface information of a fragment model. For this subject, a high-efficient point cloud feature extraction method was proposed to address a new method for extracting feature lines. First, the projection distance feature of the point cloud model was calculated to identify the potential feature points. Furthermore, the local information of the possible feature points was used to construct the adaptive neighborhoods for identifying the feature points based on neighborhoods of the model. The clustering fusion of the feature points was proposed according to the discrimination threshold values of the feature points. Finally, the Laplace operator was utilized to refine and connect the feature points to form smooth feature lines. The experimental results showed that the proposed method was automatic, highly efficient, and with good adaptability that could effectively extract the detailed features and construct the complete feature lines. Moreover, results showed that the provided framework could extract the features of simple structure models and be feasible to a certain extent for fragment models with abundant features.

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A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

Cited by 17 publications

References 26 publications

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

A Feature Extraction Algorithm Based on Optimal Neighborhood Size

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