Energy-based Models for Earth Observation Applications

Javiera Castillo-Navarro (1, 2) , Bertrand Le Saux (3) , Alexandre Boulch (4) , Sébastien Lefèvre (2)

1 DTIS, ONERA, Université Paris Saclay [Palaiseau]
2 OBELIX - Observation de l’environnement par imagerie complexe
3 ESRIN - European Space Research Institute
4 Valeo.ai

The large amount of data, available thanks to the recent sensors, have made possible the use of deep learning for Earth Observation. Yet, actual approaches tend to tackle one problem at a time, e.g. classification or image generation. We propose a new framework for Earth Observation images processing which learns an energy-based model to estimate the underlying distribution, possibly estimated using non-annotated images. On the varied image types of the EuroSAT benchmark, we show this model obtains classification results on par with state-of-the-art and moreover allows to tackle a high range of high-potential applications, from image synthesis to high performance semi-supervised learning.

Mots clés: Semi supervised Learning; Generative Models; Energy based Models; Deep Learning

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch, Sébastien Lefèvre. Energy-based Models for Earth Observation Applications. ORASIS 2021, Centre National de la Recherche Scientifique [CNRS], Sep 2021, Saint Ferréol, France. ⟨hal-03339646⟩