The results presented here are based on published work: Y. Zérah, S. Valero, and J. Inglada. « Physics-constrained deep learning for biophysical parameter retrieval from sentinel-2 images: Inversion of the prosail model« , in Remote Sensing of Environment, doi: 10.1016/j.rse.2024.114309. This work is part of the PhD of Yoël Zérah, supervised by Jordi Inglada and Silvia Valero. […]
Enhance button is a very common movies trope, where a character scrolls through some video footage or photos and asks a computer to enhance its resolution to an insane level of details, enabling solving crime mysteries and conspiracies of all sort with clues that were invisible in the original image. While this meme has been […]
The results presented in this blog are based on the published work : I.Dumeur, S.Valero, J.Inglada « Self-supervised spatio-temporal representation learning of Satellite Image Time Series » in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2024.3358066. In this paper, we describe a self-supervised learning method to train a deep neural network […]
Iota2 is constantly evolving, as you can check at the gitlab repository. Bugs fix, documentation updates and dependency version upgrade are done regularly. Also, new features are introduced such as the support of Landsat 8 & 9 images, including thermal images, or for what concerns us in this post, the support of regression models. In […]
The results presented here are based on published work : V. Bellet, M. Fauvel, J. Inglada and J. Michel, « End-to-end Learning For Land Cover Classification Using Irregular And Unaligned SITS By Combining Attention-Based Interpolation With Sparse Variational Gaussian Processes, » in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2023.3343921. This […]
In a previous post, we showed that spatio-temporal fusion methods would enable to obtain hybrid data with the revisit of the S2-NG mission (3 to 5 days), and the resolution of the Sentinel-HR mission (2 m), with an uncertainty better than the atmospheric correction errors (better than 0.01 in reflectance). In this section, we tested […]
Three years ago, we started the phase-0 study of a mission named Sentinel-HR : S-HR would be a satellite mission to complement Sentinel-2 new generation (S2-NG) with In that study, one of our assumptions was that a lower revisit frequency than that of Sentinel-2 was sufficient. This seems possible because, in most cases, the high frequency […]
=> Nous sommes heureux d’annoncer la publication de SEN2VENµS, un dataset pour l’entraînement d’algorithmes de super-résolution pour Sentinel-2 ! Julien Michel, Juan Vinasco-Salinas, Jordi Inglada, & Olivier Hagolle. (2022). SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6514159 Une description détaillée du jeu de donnée a été publié sous la […]
=> We are happy to announce the publication of SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms ! Julien Michel, Juan Vinasco-Salinas, Jordi Inglada, & Olivier Hagolle. (2022). SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6514159 A detailed description of the dataset has been published as a […]