Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 541–548, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 541–548, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Aug 2019

22 Aug 2019

GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

E. Grilli1,2, E. M. Farella1, A. Torresani1,3, and F. Remondino1 E. Grilli et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2University of Bologna, Italy
  • 3University of Trento, Italy

Keywords: Point clouds, Covariance features, Random Forest, Cultural Heritage

Abstract. In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.