Distributed Boosting for Cloud Detection

Matthieu Le Goff (1, 2, 3) , Jean-Yves Tourneret (4, 2, 5) , Herwig Wendt (6, 2, 7) , Mathias Ortner (3) , Marc Spigai (3)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 TéSA - Télécommunications Spatiales et Aéronautiques - Telecommunications for Space ant Aeronautics
3 IRT Saint Exupéry - Institut de Recherche Technologique
4 IRIT-MINDS - CoMputational imagINg anD viSion
5 Toulouse INP - Institut National Polytechnique (Toulouse)
6 IRIT-SC - Signal et Communications
7 CNRS - Centre National de la Recherche Scientifique

The SPOT 6-7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. In order to significantly improve the SPOT cloud detection and get rid of frequent manual re-labelings, we study a new automatic cloud detection technique that is adapted to large datasets. The proposed method is based on a modified distributed boosting algorithm. Experiments conducted using the framework Apache Spark on a SPOT 6 image database with various landscapes and cloud coverage show promising results.

Mots clés: Distributed processing; Big data; Cloud detection; Remote sensing; Boosting

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Matthieu Le Goff, Jean-Yves Tourneret, Herwig Wendt, Mathias Ortner, Marc Spigai. Distributed Boosting for Cloud Detection. International Geoscience and Remote Sensing Symposium (IGARSS 2016), Jul 2016, Beijing, China. pp. 2626-2629. ⟨hal-01447350⟩