Deep learning for urban remote sensing

Nicolas Audebert 1, 2 Alexandre Boulch 2 Hicham Randrianarivo 2 Bertrand Le Saux 2 Marin Ferecatu 3 Sébastien Lefèvre 1 Renaud Marlet 4, 5
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 ONERA - The French Aerospace Lab [Palaiseau]
3 CEDRIC - Centre d'études et de recherche en informatique et communications
4 LIGM - Laboratoire d'Informatique Gaspard-Monge
5 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, ENPC - École des Ponts ParisTech
Abstract : This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.
Keywords : Training Remote sensing Labeling Three-dimensional displays Semantics Feature extraction learning (artificial intelligence) image classification Hough transforms Support vector machines
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Conference papers
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ENPC | INSTITUT-TELECOM | IRISA_UBS | CENTRALESUPELEC | UNIV-PARIS-SACLAY | UR1-UFR-ISTIC | INRIA2017 | UNIV-RENNES | IMAGINE | INRIA | UNIV-RENNES1 | IRISA | PARISTECH | ONERA | UPEC-UPEM | LIGM | LIGM_A3SI | ESIEE-PARIS | UNIV-HESAM | IRISA-D5

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Nicolas Audebert, Alexandre Boulch, Hicham Randrianarivo, Bertrand Le Saux, Marin Ferecatu, et al.. Deep learning for urban remote sensing. Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. ⟨10.1109/JURSE.2017.7924536⟩. ⟨hal-01672854⟩

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