The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021

  28 Jun 2021

28 Jun 2021

AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

A. Nurunnabi1, F. N. Teferle1, J. Li2, R. C. Lindenbergh3, and A. Hunegnaw1 A. Nurunnabi et al.
  • 1Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6, rue Richard Codenhove-Kalergi, L-1359, Luxembourg
  • 2Geography and Environmental Management, University of Waterloo, Waterloo ON N2L 3G1, Canada
  • 3Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands

Keywords: Classification, CNN, Feature Extraction, LiDAR, Local Feature, Neural Network, PointNet, Semantic Analysis

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.