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

Zaide Duran,Muhammed Enes Atik,Kubra Ozcan

doi:10.3390/drones5040104

Zaide Duran, Muhammed Enes Atik + Show 1 more

Open Access

https://doi.org/10.3390/drones5040104

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Journal: Drones	Publication Date: Sep 24, 2021
Citations: 9	License type: CC BY 4.0

Affiliation: Istanbul Technical University

Abstract

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.

Highlights

Extraction of information from three-dimensional (3D) data has become a popular research area in photogrammetry, remote sensing, computer vision and robotics
Machine learning algorithms have been applied to the Light Detection and Ranging (LiDAR) dataset and the photogrammetric point cloud dataset
According to the data obtained from the LiDAR point cloud, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method and 0.95 with the K-Nearest Neighbors (K-NN) method

Summary

Introduction

Extraction of information from three-dimensional (3D) data has become a popular research area in photogrammetry, remote sensing, computer vision and robotics. Classification of point clouds is a challenging field of study, due to their complex structure. Photogrammetric methods, Light Detection and Ranging (LiDAR) systems, Red Green Blue Depth (RGB-D) cameras and Synthetic Aperture Radar (SAR) are methods used to obtain point clouds [1,2]. Machine learning is defined as a mathematical tool for classification of the complex content of point clouds, as in 2D optical images. Classification rules are learned automatically using training data, rather than defined as parameters based on predetermined rules and strong assumptions. Much of the challenging design in a rule-based classification technique is avoided due to automatic feature selection in machine learning. Machine learning methods are more appropriate than traditional classification methods for complex point cloud data [5]

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Conclusion