3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data

Mathilde Letard (1) , Dimitri Lague (1) , Arthur Le Guennec (1) , Sébastien Lefèvre (2) , Baptiste Feldmann (1) , Paul Leroy (1) , Daniel Girardeau-Montaut (3) , Thomas Corpetti (4)

1 GR - Géosciences Rennes
2 OBELIX - Observation de l’environnement par imagerie complexe
3 Johnson & Johnson Corporation
4 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique

Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods, for example, new topo-bathymetric lidar data. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow introduces multi-cloud classification through dual-cloud features, encrypting local spectral and geometrical ratios and differences. 3DMASC uses classical multi-scale descriptors adapted to all types of 3D point clouds and new ones based on their spatial variations. In this paper, we present the performances of 3DMASC for multi-class classification of topo-bathymetric lidar data in coastal and fluvial environments. We show how multivariate and embedded feature selection allows the building of optimized predictor sets of reduced complexity, and we identify features particularly relevant for coastal and riverine scene descriptions. Our results show the importance of dual-cloud features, lidar return-based attributes averaged over specific scales, and of statistics of dimensionality-based and spectral features. Additionally, they indicate that small to medium spherical neighbourhood diameters (<7 m) are sufficient to build effective classifiers, namely when combined with distance-to-ground or distance-to-water-surface features. Without using optional RGB information, and with a maximum of 37 descriptors, we obtain classification accuracies between 91 % for complex multi-class tasks and 98 % for lower-level processing using models trained on less than 2000 samples per class. Comparisons with classical point cloud classification methods show that 3DMASC features have a significantly improved descriptive power. Our contributions are made available through a plugin in the CloudCompare software, allowing non-specialist users to create classifiers for any type of 3D data characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure from motion), and two labelled topo-bathymetric lidar datasets, available on https://opentopography.org/.

Mots clés: bi-spectral lidar; multi-scale classification; multi-cloud classification; feature selection; 3D data; machine learning

Domaines: Machine Learning [stat.ML]; Sciences de l'environnement

Mathilde Letard, Dimitri Lague, Arthur Le Guennec, Sébastien Lefèvre, Baptiste Feldmann, et al.. 3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 207, pp.175-197. ⟨10.1016/j.isprsjprs.2023.11.022⟩. ⟨hal-04353070v3⟩