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A multi-task deep learning framework coupling semantic segmentation and image reconstruction for very high resolution imagery

Maria Papadomanolaki 1, 2, 3 Konstantinos Karantzalos 4 Maria Vakalopoulou 5, 3
1 CentraleSupélec
2 NTUA - National Technical University of Athens [Athens]
3 OPIS - OPtimisation Imagerie et Santé
Inria Saclay - Ile de France, CVN - Centre de vision numérique
4 Remote Sensing Laboratory
5 ENPC - École des Ponts ParisTech
Keywords : Fully-convolutional networks Autoencoders Limited annotations Feature representations Deep learning
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https://hal.inria.fr/hal-02266085
Contributor : Maria Papadomanolaki <>
Submitted on : Tuesday, August 13, 2019 - 12:24:05 PM
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  • HAL Id : hal-02266085, version 1

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ENPC | CENTRALESUPELEC | INRIA | PARISTECH | UNIV-PARIS-SACLAY | CVN

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Maria Papadomanolaki, Konstantinos Karantzalos, Maria Vakalopoulou. A multi-task deep learning framework coupling semantic segmentation and image reconstruction for very high resolution imagery. IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨hal-02266085⟩

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