Abdeldjalil Aïssa-El-Bey

Learning sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

By Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Abdeldjalil Aïssa El Bey

2023-05-29

In Proceedings of the 31th european signal processing conference (EUSIPCO)

Abstract

Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth’s surface have been made publicly available for scientific purpose, for example through the European Copernicus project. Simultaneously, the development of self-supervised learning (SSL) methods has sparked great interest in the remote sensing community, enabling to learn latent representations from unlabeled data to help treating downstream tasks for which there is few annotated examples, such as interpolation, forecasting or unmixing. Following this line, we train a deep learning model inspired from the Koopman operator theory to model long-term reflectance dynamics in an unsupervised way. We show that this trained model, being differentiable, can be used as a prior for data assimilation in a straightforward way. Our datasets, which are composed of Sentinel-2 multispectral image time series, are publicly released with several levels of treatment.

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Leveraging neural koopman operators to learn continuous representations of dynamical systems from scarce data

By Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aïssa-El-Bey

2023-02-17

In Proceedings of the 48th IEEE international conference on acoustics, speech, and signal processing (ICASSP)

Abstract

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.

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Assimilation de données variationnelle de séries temporelles d’images sentinel-2 avec un modèle dynamique auto-supervisé

By Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aïssa-El-Bey

2022-06-14

In 29e colloque sur le traitement du signal et des images

Abstract

Au cours des dernières années, l’apprentissage profond a acquis une importance croissante dans de nombreux domaines scientifiques, notamment en ce qui concerne le traitement d’images, et en particulier pour le traitement des données issues de satellites. Le paradigme le plus courant en ce qui concerne l’apprentissage profond est l’apprentissage supervisé, qui requiert une grande quantité de données annotées représentant la vérité terrain pour la tâche d’intérêt. Or, obtenir des données correctement annotées pose souvent des difficultés financières ou techniques importantes. Pour cette raison, nous nous plaçons ici dans le cadre de l’apprentissage auto-supervisé. Nous proposons un modèle d’apprentissage profond inspiré de la théorie de l’opérateur de Koopman qui apprend, à partir de séries temporelles d’images multispectrales Sentinel-2, à modéliser les dynamiques de long terme de réflectance des pixels. Après son entraînement, notre modèle peut être utilisé dans divers problèmes inverses faisant intervenir la dynamique temporelle pour résoudre différentes tâches telles que l’interpolation ou le débruitage de données.

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