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Communication Dans Un Congrès Année : 2021

Classification and Generation of Earth Observation Images using a Joint Energy-Based Model

Résumé

Deep learning has changed unbelievably the processing of Earth Observation tasks such as land cover mapping or image registration. Yet, today new models are needed to push further the revolution and enable new possibilities. We propose a new framework for generative modelling of Earth Observation images. It learns an energy-based model to estimate the underlying distribution of the data while jointly training a deep neural network for classification. 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 us to tackle a wide range of high-potential applications: image synthesis, out-of-distribution testing for domain adaptation, and image completion or denoising.
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Dates et versions

hal-03379992 , version 1 (15-10-2021)

Identifiants

  • HAL Id : hal-03379992 , version 1

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Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch, Sébastien Lefèvre. Classification and Generation of Earth Observation Images using a Joint Energy-Based Model. IGARSS 2021 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2021, Brussels, France. ⟨hal-03379992⟩
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