24-097 Integration of Thermal Imaging and Radar for
Il y a 2 mois
Doctorat, 36 mois
- Temps plein
- Aucune expérience exigée
- Maitrise, IEP, IUP, Bac+4
- Digital technologies for remote sensing
**Mission**:
This doctoral project is part of the Onera "MUSIC" research chair, led by Elise Colin, which focuses on developing innovative image processing and AI techniques for unconventional data.
It explores the complementarity between thermal imaging (IRT) and Synthetic Aperture Radar (SAR) to understand land surface properties better. IRT data provides measurements of Land Surface Temperature (LST) and emissivity, while SAR data measures radar backscatter, which depends on the characteristics of observed materials. This data fusion creates new opportunities for improved environmental characterization. The thesis focuses on using deep learning to:
- fill gaps in LST data using spatiotemporal fusion and super-resolution.
- improve estimation of LST from radar backscatter,
- improve the simulation of radar images from IRT data.
The doctoral student will first receive training on available thermal and radar data and will compile a bibliography on data fusion focusing on spatiotemporal analysis. Based on existing work and data analysis, they will identify deep learning models to explore the mentioned approaches.
These thesis activities will be conducted in a transdisciplinary framework, within DTIS in Palaiseau, with close collaboration with DOTA in Toulouse and IMT Atlantique/LabSTICC in Brest. The student may be required to travel between the respective teams.
References
Letheule, N., Weissgerber, F., Lobry, S., and Colin, E., Automatic simulation of SAR images: comparing a deep-learning based method to a hybrid method, In IEEE International Geoscience and Remote Sensing Symposium IGARSS 2023
Wu, P., Yin, Z., Zeng, C., Duan, S. B., Göttsche, F. M., Ma, X.,... & Shen, H. (2021). Spatially continuous and high-resolution land surface temperature product generation: A review of reconstruction and spatiotemporal fusion techniques. IEEE Geoscience and Remote Sensing Magazine, 9(3), 112-137.
Nguyen, B. M., Tian, G., Vo, M. T., Michel, A., Corpetti, T., & Granero-Belinchon, C. (2022, August). Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution. In 2022 30th European Signal Processing Conference (EUSIPCO) (pp. 1806-1810). IEEE.
Di Martino, T., Guinvarc’h, R., Thirion-Lefevre, L., & Colin, É. (2023). Grad-SLAM: Explaining Convolutional Autoencoders’ Latent Space of Satellite Image Time Series. IEEE Geoscience and Remote Sensing Letters.
Notarnicola, C., Angiulli, M., & Posa, F. (2008). Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 547-557.
Fablet, R., Febvre, Q., & Chapron, B. (2023). Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies. IEEE Transactions on Geoscience and Remote Sensing.
=================
**Profil**:
remote sensing, signal processing, or data science with interest in Earth Observation
**Laboratoire**:
ONERA
**MESSAGE from Phd Team**:
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