Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 3-10, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-3-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 3-10, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-3-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

CLASSIFICATION OF AERIAL PHOTOGRAMMETRIC 3D POINT CLOUDS

C. Becker1, N. Häni2, E. Rosinskaya1, E. d’Angelo1, and C. Strecha1 C. Becker et al.
  • 1Pix4D SA, EPFL Innovation Park, Building F, 1015 Lausanne, Switzerland
  • 2University of Minnesota, USA

Keywords: Semantic Classification, Aerial Photogrammetry, LiDAR, Point Clouds, Photogrammetry

Abstract. We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.