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 Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

2019 ◽  
Vol 11 (23) ◽  
pp. 2846 ◽  
Author(s):  
Tong ◽  
Li ◽  
Zhang ◽  
Chen ◽  
Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Single Point ◽  
Point Clouds ◽  
Max Pooling ◽  
Multi Scale ◽  
Cloud Classification ◽  
Point Set ◽  
Multi Level ◽  
Point Cloud Classification

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

scholarly journals POINT CLOUD CLASSIFICATION BY FUSING SUPERVOXEL SEGMENTATION WITH MULTI-SCALE FEATURES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 919-925
Author(s):  
W. Ao ◽  
L. Wang ◽  
J. Shan
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Geometric Information ◽  
Multi Scale ◽  
Cloud Classification ◽  
Spatial Consistency ◽  
Class Labels ◽  
Point Cloud Classification ◽  
Robust To Noise

<p><strong>Abstract.</strong> Point cloud classification is quite a challenging task due to the existence of noises, occlusion and various object types and sizes. Currently, the commonly used statistics-based features cannot accurately characterize the geometric information of a point cloud. This limitation often leads to feature confusion and classification mistakes (e.g., points of building corners and vegetation always share similar statistical features in a local neighbourhood, such as curvature, sphericity, etc). This study aims at solving this problem by leveraging the advantage of both the supervoxel segmentation and multi-scale features. For each point, its multi-scale features within different radii are extracted. Simultaneously, the point cloud is partitioned into simple supervoxel segments. After that, the class probability of each point is predicted by the proposed SegMSF approach that combines multi-scale features with the supervoxel segmentation results. At the end, the effect of data noises is supressed by using a global optimization that encourages spatial consistency of class labels. The proposed method is tested on both airborne laser scanning (ALS) and mobile laser scanning (MLS) point clouds. The experimental results demonstrate that the proposed method performs well in terms of classifying objects of different scales and is robust to noise.</p>

scholarly journals Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

2021 ◽  
Vol 10 (7) ◽  
pp. 444
Author(s):  
Jianfeng Zhu ◽  
Lichun Sui ◽  
Yufu Zang ◽  
He Zheng ◽  
Wei Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolution Operator ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Cloud Classification ◽  
Point Cloud Classification

In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.

scholarly journals Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method

2021 ◽  
Vol 13 (5) ◽  
pp. 859
Author(s):  
Elyta Widyaningrum ◽  
Qian Bai ◽  
Marda K. Fajari ◽  
Roderik C. Lindenbergh
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Block Size ◽  
Point Clouds ◽  
Learning Method ◽  
Cloud Classification ◽  
Indoor Scenes ◽  
Point Cloud Classification

Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise deep learning method Dynamic Graph Convolutional Neural Network (DGCNN) and extend its classification application from indoor scenes to airborne point clouds. This study proposes an approach to provide cheap training samples for point-wise deep learning using an existing 2D base map. Furthermore, essential features and spatial contexts to effectively classify airborne point clouds colored by an orthophoto are also investigated, in particularly to deal with class imbalance and relief displacement in urban areas. Two airborne point cloud datasets of different areas are used: Area-1 (city of Surabaya—Indonesia) and Area-2 (cities of Utrecht and Delft—the Netherlands). Area-1 is used to investigate different input feature combinations and loss functions. The point-wise classification for four classes achieves a remarkable result with 91.8% overall accuracy when using the full combination of spectral color and LiDAR features. For Area-2, different block size settings (30, 50, and 70 m) are investigated. It is found that using an appropriate block size of, in this case, 50 m helps to improve the classification until 93% overall accuracy but does not necessarily ensure better classification results for each class. Based on the experiments on both areas, we conclude that using DGCNN with proper settings is able to provide results close to production.

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 ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram

2018 ◽  
Vol 45 (10) ◽  
pp. 1004001
Author(s):  
佟国峰 Tong Guofeng ◽  
杜宪策 Du Xiance ◽  
李勇 Li Yong ◽  
陈槐嵘 Chen Huairong ◽  
张庆春 Zhang Qingchun
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Slice Sampling ◽  
Cloud Classification ◽  
Centroid Distance ◽  
Outdoor Scenes ◽  
Vertical Slice ◽  
Point Cloud Classification ◽  
Distance Histogram

scholarly journals Classification of Point Clouds for Indoor Components Using Few Labeled Samples

2020 ◽  
Vol 12 (14) ◽  
pp. 2181
Author(s):  
Hangbin Wu ◽  
Huimin Yang ◽  
Shengyu Huang ◽  
Doudou Zeng ◽  
Chun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Neighborhood Search ◽  
Learning Methods ◽  
Semantic Classification ◽  
Cloud Classification ◽  
Mixed Features ◽  
Indoor Scenarios ◽  
Point Cloud Classification

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

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

2019 ◽  
Vol 9 (5) ◽  
pp. 951 ◽  
Author(s):  
Yong Li ◽  
Guofeng Tong ◽  
Xiance Du ◽  
Xiang Yang ◽  
Jianjun Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Single Point ◽  
Point Clouds ◽  
Optimization Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Angle Distribution ◽  
Features Fusion ◽  
Cloud Classification ◽  
Point Cloud Classification

3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

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