scholarly journals Classification of Handheld Laser Scanning Tree Point Cloud Based on Different KNN Algorithms and Random Forest Algorithm

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 292
Author(s):  
Wenshu Lin ◽  
Weiwei Fan ◽  
Haoran Liu ◽  
Yongsheng Xu ◽  
Jinzhuo Wu
Keyword(s):  
Random Forest ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Laser Scanning ◽  
Training Sample ◽  
Classification Model ◽  
Cloud Data ◽  
Sample Classification ◽  
Adaptive Radius

Handheld mobile laser scanning (HMLS) can quickly acquire point cloud data, and has the potential to conduct forest inventory at the plot scale. Considering the problems associated with HMLS data such as large discreteness and difficulty in classification, different classification models were compared in order to realize efficient separation of stem, branch and leaf points from HMLS data. First, the HMLS point cloud was normalized and ground points were removed, then the neighboring points were identified according to three KNN algorithms and eight geometric features were constructed. On this basis, the random forest classifier was used to calculate feature importance and perform dataset training. Finally, the classification accuracy of different KNN algorithms-based models was evaluated. Results showed that the training sample classification accuracy based on the adaptive radius KNN algorithm was the highest (0.9659) among the three KNN algorithms, but its feature calculation time was also longer; The validation accuracy of two test sets was 0.9596 and 0.9201, respectively, which is acceptable, and the misclassification mainly occurred in the branch junction of the canopy. Therefore, the optimal classification model can effectively achieve the classification of stem, branch and leaf points from HMLS point cloud under the premise of comprehensive training.

A study on the classification of vegetation point cloud based on the straw checkerbo-ard barriers area of random forest

2021 ◽  
pp. 1-13
Author(s):  
Tiebo Sun ◽  
Jinhao Liu ◽  
Jiangming Kan ◽  
Tingting Sui
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Random Forest Algorithm ◽  
Cloud Data ◽  
Cloud Classification ◽  
Point Cloud Classification

Aiming at the problem of automatic classification of point cloud in the investigation of vegetation resources in the straw checkerboard barriers region, an improved random forest point cloud classification algorithm was proposed. According to the problems of decision tree redundancy and absolute majority voting in the existing random forest algorithm, first the similarity of the decision tree was calculated based on the tree edit distance, further clustered reduction based on the maximum and minimum distance algorithm, and then introduced classification accuracy of decision tree to construct weight matrix to implement weighted voting at the voting stage. Before random forest classification, based on the characteristics of point cloud data, a total of 20 point cloud single-point features and multi-point statistical features were selected to participate in point cloud classification, based on the point cloud data spatial distribution characteristics, three different scales for selecting point cloud neighborhoods were set based on the point cloud density, point cloud classification feature sets at different scales were constructed, optimizing important features of point cloud to participate in point cloud classification calculation after variable importance scored. The experimental results showed that the point cloud classification based on the optimized random forest algorithm in this paper achieved a total classification accuracy of 94.15% in dataset 1 acquired by lidar, the overall accuracy of classification on dataset 2 obtained by dense matching reaches 92.03%, both were higher than the unoptimized random forest algorithm and MRF, SVM point cloud classification method, and dimensionality reduction through feature optimization can greatly improve the efficiency of the algorithm.

An approach to classification of airborne laser scanning point cloud data in an urban environment

2011 ◽  
Vol 32 (24) ◽  
pp. 9151-9169 ◽  
Author(s):  
Cici Alexander ◽  
Kevin Tansey ◽  
Jörg Kaduk ◽  
David Holland ◽  
Nicholas J. Tate
Keyword(s):  
Urban Environment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Airborne Laser

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.

Effectiveness of Training Sample and Features for Random Forest on Road Extraction from Unmanned Aerial Vehicle-Based Point Cloud

2021 ◽  
pp. 036119812110296
Author(s):  
Serkan Biçici ◽  
Mustafa Zeybek
Keyword(s):  
Random Forest ◽  
Unmanned Aerial Vehicle ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Training Sample ◽  
Road Surface ◽  
Learning Stage ◽  
Training Samples ◽  
Aerial Vehicle

The accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%–13%, and 95% overall accuracy was obtained when using convenient training samples and features.

scholarly journals POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 445-452 ◽  
Author(s):  
M. Soilán ◽  
R. Lindenbergh ◽  
B. Riveiro ◽  
A. Sánchez-Rodríguez
Keyword(s):  
Deep Learning ◽  
Laser Scanning ◽  
False Negative ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Study Data ◽  
Classification Model ◽  
Ground Vegetation ◽  
Cloud Data

<p><strong>Abstract.</strong> During the last couple of years, there has been an increased interest to develop new deep learning networks specifically for processing 3D point cloud data. In that context, this work intends to expand the applicability of one of these networks, PointNet, from the semantic segmentation of indoor scenes, to outdoor point clouds acquired with Airborne Laser Scanning (ALS) systems. Our goal is to of assist the classification of future iterations of a national wide dataset such as the <i>Actueel Hoogtebestand Nederland</i> (AHN), using a classification model trained with a previous iteration. First, a simple application such as ground classification is proposed in order to prove the capabilities of the proposed deep learning architecture to perform an efficient point-wise classification with aerial point clouds. Then, two different models based on PointNet are defined to classify the most relevant elements in the case study data: Ground, vegetation and buildings. While the model for ground classification performs with a F-score metric above 96%, motivating the second part of the work, the overall accuracy of the remaining models is around 87%, showing consistency across different versions of AHN but with improvable false positive and false negative rates. Therefore, this work concludes that the proposed classification of future AHN iterations is feasible but needs more experimentation.</p>

scholarly journals JOINT CLASSIFICATION OF ALS AND DIM POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1113-1120
Author(s):  
F. Politz ◽  
M. Sester
Keyword(s):  
Transfer Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Measurement Techniques ◽  
Point Clouds ◽  
Aerial Images ◽  
Individual Measurement ◽  
Cloud Data ◽  
Practical Applications

<p><strong>Abstract.</strong> National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important data source for this task; recently, NMAs also started deriving Dense Image Matching (DIM) point clouds from aerial images. As a result, NMAs have both point cloud data sources available, which they can exploit for their purposes. In this study, we investigate the potential of transfer learning from ALS to DIM data, so the time consuming step of data labelling can be reduced. Due to their specific individual measurement techniques, both point clouds have various distinct properties such as RGB or intensity values, which are often exploited for classification of either ALS or DIM point clouds. However, those features also hinder transfer learning between these two point cloud types, since they do not exist in the other point cloud type. As the mere 3D point is available in both point cloud types, we focus on transfer learning from an ALS to a DIM point cloud using exclusively the point coordinates. We are tackling the issue of different point densities by rasterizing the point cloud into a 2D grid and take important height features as input for classification. We train an encoder-decoder convolutional neural network with labelled ALS data as a baseline and then fine-tune this baseline with an increasing amount of labelled DIM data. We also train the same network exclusively on all available DIM data as reference to compare our results. We show that only 10% of labelled DIM data increase the classification results notably, which is especially relevant for practical applications.</p>

scholarly journals Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4750
Author(s):  
Jianchang Chen ◽  
Yiming Chen ◽  
Zhengjun Liu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Network Mapping

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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