scholarly journals OBJECT-BASED CLASSIFICATION OF URBAN AIRBORNE LIDAR POINT CLOUDS WITH MULTIPLE ECHOES USING SVM

2012 ◽  
Vol I-3 ◽  
pp. 135-140 ◽  
Author(s):  
J. X. Zhang ◽  
X. G. Lin
Keyword(s):  
Point Clouds ◽  
Airborne Lidar ◽  
Object Based

scholarly journals Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 104
Author(s):  
Zaide Duran ◽  
Kubra Ozcan ◽  
Muhammed Enes Atik
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Airborne Lidar ◽  
Aerial Photogrammetry ◽  
Feature Spaces ◽  
Wide Range ◽  
Extract Information

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy.

Tree segmentation and classification of deciduous park trees in Sanssouci Park, Potsdam, Germany, using airborne and terrestrial lidar point clouds

2021 ◽  
Author(s):  
Aljoscha Rheinwalt ◽  
Bodo Bookhagen
Keyword(s):  
Tree Species ◽  
Supervised Classification ◽  
Spatial Scales ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Deciduous Tree ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Common Tree

<p>While automated, lidar-based tree delineation has proven successful for<br>conifer-dominated forests, deciduous tree stands remain a challenge.  But<br>automatic and reliable segmentation of trees at large spatial scales is a<br>prerequisite for a supervised classification into tree species. We propose an<br>aspect driven tree segmentation that clusters local elevation minima across<br>different aspects. These clusters define tree outlines that respect tree<br>inherent local elevation minima. We validate this approach with more than<br>25.000 mapped trees of the Sanssouci Park, Potsdam, using an airborne lidar<br>point cloud collected in 2018, and various terrestrial lidar scans for a large<br>fraction of the same park. Further, we demonstrate the tree segmentation by<br>supervised tree species classifications for the most common tree species using<br>random forests and Gaussian process classifiers with geometric parameters<br>derived from individual tree crowns.</p>

scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

scholarly journals COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 113-118
Author(s):  
M. Bouziani ◽  
M. Amraoui ◽  
S. Kellouch
Keyword(s):  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Models ◽  
Pulse Frequency ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Surface Model ◽  
Digital Products

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

scholarly journals HIERARCHICAL HIGHER ORDER CRF FOR THE CLASSIFICATION OF AIRBORNE LIDAR POINT CLOUDS IN URBAN AREAS

2016 ◽  
Vol XLI-B3 ◽  
pp. 655-662 ◽  
Author(s):  
J. Niemeyer ◽  
F. Rottensteiner ◽  
U. Soergel ◽  
C. Heipke
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Conditional Random Field ◽  
Point Clouds ◽  
Higher Order ◽  
Airborne Lidar ◽  
Classification Result ◽  
3D Lidar ◽  
Point Level

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the <i>distance</i> and the <i>orientation of a segment with respect to the closest road</i>. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

scholarly journals SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

2013 ◽  
Vol 5 (8) ◽  
pp. 3749-3775 ◽  
Author(s):  
Jixian Zhang ◽  
Xiangguo Lin ◽  
Xiaogang Ning
Keyword(s):  
Urban Areas ◽  
Point Clouds ◽  
Airborne Lidar
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