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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