scholarly journals Comparison of Different Feature Sets for TLS Point Cloud Classification

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4206 ◽  
Author(s):  
Quan Li ◽  
Xiaojun Cheng
Keyword(s):  
Feature Selection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Geometric Features ◽  
Real World Data ◽  
Cloud Data ◽  
Cloud Classification ◽  
Color Features ◽  
Point Cloud Classification ◽  
Corrected Intensity

Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by Terrestrial laser scanning (TLS). Neighborhood selection, feature selection and extraction, and classification of points based on the respective features constitute the commonly used workflow of point cloud classification. Feature selection and extraction has been the focus of many studies, and the choice of different features has had a great impact on classification results. In previous studies, geometric features were widely used for TLS point cloud classification, and only a few studies investigated the potential of both intensity and color on classification using TLS point cloud. In this paper, the geometric features, color features, and intensity features were extracted based on a supervoxel neighborhood. In addition, the original intensity was also corrected for range effect, which is why the corrected intensity features were also extracted. The different combinations of these features were tested on four real-world data sets. Experimental results demonstrate that both color and intensity features can complement the geometric features to help improve the classification results. Furthermore, the combination of geometric features, color features, and corrected intensity features together achieves the highest accuracy in our test.

scholarly journals COMPARISON OF POINT AND SEGMENT BASED POINT CLOUD CLASSIFICATION METHOD IN URBAN SCENES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 461-465
Author(s):  
E. Hasanpour ◽  
M. Saadatseresht ◽  
E. G. Parmehr
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Classification Method ◽  
Geometric Features ◽  
Classification Result ◽  
Urban Scenes ◽  
Cloud Data ◽  
Random Forest Classification ◽  
Cloud Classification ◽  
Point Cloud Classification

Abstract. Point cloud classification is an essential requirement for effectively utilizing point cloud data acquired by different kind of sources such as Terrestrial Laser Scanning (TLS), Aerial LiDAR (Light Detection and Ranging), and Photogrammetry. Classification of point cloud is a process that points are separated into different point groups that each group has similar features. Point cloud classification can be done in three levels (point-based, segment-based, and object-based) and the choice of different level has significant impact on classification result. In this research, random forest classification method is utilized in which the point-wise and segment-wise spectral and geometric features are selected as the input of the classification. In our experiments, the results of point- and segment-based classification were compared. In addition, point-wise classification result for two different features (geometric with/without spectral features) has been compared and the results are presented. The experiments illustrated that segment based classification with both color and geometric features has the best overall accuracy of 83% especially near the object boundaries.

scholarly journals Manual Point Cloud Classification and Extraction for Hunter-Gatherer Feature Investigation: A Test Case From Two Low Arctic Paleo-Inuit Sites

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
David B. Landry ◽  
S. Brooke Milne ◽  
Robert W. Park ◽  
Ian J. Ferguson ◽  
Mostafa Fayek
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Test Case ◽  
Cloud Data ◽  
Cloud Classification ◽  
Low Arctic ◽  
Point Cloud Classification ◽  
Effective Use

AbstractFor archaeologists, the task of processing large terrestrial laser scanning (TLS)-derived point cloud data can be difficult, particularly when focusing on acquiring analytical and interpretive outcomes from the data. Using our TLS lidar data collected in 2013 from two compositionally different, low Arctic multi-component hunter-gatherer sites (LdFa-1 and LeDx-42), we demonstrate how a manual point cloud classification approach with open source software can be used to extract natural and archaeological features from a site’s surface. Through a combination of spectral datasets typical to TLS (i.e., intensity and RGB values), archaeologists can enhance the visual and analytical representation of archaeological huntergatherer site surfaces. Our approach classifies low visibility Arctic site point clouds into independent segments, each representing a different surface material found on the site. With the segmented dataset, we extract only the surface boulders to create an alternate characterization of the site’s prominent features and their surroundings. Using surface point clouds from Paleo-Inuit sites allows us to demonstrate the value of this approach within hunter-gatherer research as our results illustrate an effective use of large TLS datasets for extracting and improving our analytical capabilities for low relief site features.

scholarly journals Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

2018 ◽  
Vol 10 (8) ◽  
pp. 1192 ◽  
Author(s):  
Chen-Chieh Feng ◽  
Zhou Guo
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Parameter Learning ◽  
Cloud Classification ◽  
Land Cover Map ◽  
Point Cloud Classification ◽  
Optimal Feature

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

scholarly journals GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 157-164 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi ◽  
C. Mallet ◽  
M. Weinmann
Keyword(s):  
Influencing Factors ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Sampling ◽  
Cloud Data ◽  
Semantic Classes ◽  
Automatic Interpretation ◽  
Point Cloud Classification ◽  
The Impact

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (<i>linear structures, planar structures</i> and <i>volumetric structures</i>) as well as a reference labeling with respect to five semantic classes (<i>Wire, Pole/Trunk, Façade, Ground</i> and <i>Vegetation</i>) is available.

scholarly journals Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

2021 ◽  
Vol 13 (16) ◽  
pp. 3156
Author(s):  
Yong Li ◽  
Yinzheng Luo ◽  
Xia Gu ◽  
Dong Chen ◽  
Fang Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Local Binary Pattern ◽  
Random Forest Classifier ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Color Information ◽  
Cloud Data ◽  
Cloud Classification ◽  
Local Difference ◽  
Point Cloud Classification

Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification.

scholarly journals Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification

2019 ◽  
Vol 11 (3) ◽  
pp. 342 ◽  
Author(s):  
Zongxia Xu ◽  
Zhenxin Zhang ◽  
Ruofei Zhong ◽  
Dong Chen ◽  
Taochun Sun ◽  
...  
Keyword(s):  
Density Distribution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Latent Dirichlet Allocation ◽  
Voronoi Tessellation ◽  
Hierarchical Classification ◽  
Initial Point ◽  
Complex Scene ◽  
Cloud Classification ◽  
Point Cloud Classification

Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques.

scholarly journals Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1222 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Xiong Li ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Point Cloud ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Power Lines ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Cloud Data ◽  
Line Classification

Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

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