A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION

As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.

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Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

Sensors ◽

10.3390/s19214717 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4717 ◽

Cited By ~ 2

Author(s):

Yuxuan Liu ◽

Mitko Aleksandrov ◽

Sisi Zlatanova ◽

Junjun Zhang ◽

Fan Mo ◽

...

Keyword(s):

Transmission Lines ◽

Point Cloud ◽

Power Transmission ◽

Machine Learning Algorithms ◽

Power Transmission Lines ◽

Topological Constraints ◽

Adaboost Algorithm ◽

Cloud Classification ◽

Point Cloud Classification

Machine learning algorithms can be well suited to LiDAR point cloud classification, but when they are applied to the point cloud classification of power facilities, many problems such as a large number of computational features and low computational efficiency can be encountered. To solve these problems, this paper proposes the use of the Adaboost algorithm and different topological constraints. For different objects, the top five features with the best discrimination are selected and combined into a strong classifier by the Adaboost algorithm, where coarse classification is performed. For power transmission lines, the optimum scales are selected automatically, and the coarse classification results are refined. For power towers, it is difficult to distinguish the tower from vegetation points by only using spatial features due to the similarity of their proposed key features. Therefore, the topological relationship between the power line and power tower is introduced to distinguish the power tower from vegetation points. The experimental results show that the classification of power transmission lines and power towers by our method can achieve the accuracy of manual classification results and even be more efficient.

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Adaptive neighborhood size and effective geometric features selection for 3D scattered point cloud classification

Applied Soft Computing ◽

10.1016/j.asoc.2021.108196 ◽

2021 ◽

pp. 108196

Author(s):

Mehmet Akif Gunen

Keyword(s):

Point Cloud ◽

Neighborhood Size ◽

Geometric Features ◽

Features Selection ◽

Cloud Classification ◽

Selection For ◽

Adaptive Neighborhood ◽

Point Cloud Classification

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A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Mathematics ◽

10.3390/math9121328 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1328

Author(s):

Manuel Oviedo-de la Fuente ◽

Carlos Cabo ◽

Celestino Ordóñez ◽

Javier Roca-Pardiñas

Keyword(s):

Point Cloud ◽

Multinomial Logistic Regression ◽

Machine Learning Algorithms ◽

Support Vector ◽

Dc Functions ◽

Data Set ◽

Distance Correlation ◽

3D Point Clouds ◽

Cloud Classification ◽

Point Cloud Classification

Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.

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