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

Maria Papadomanolaki; Konstantinos Karantzalos; Maria Vakalopoulou

Conference papers

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Computer Vision and Pattern Recognition [cs.CV]

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https://hal.inria.fr/hal-02266085
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Submitted on : Tuesday, August 13, 2019 - 12:24:05 PM
Last modification on : Saturday, May 1, 2021 - 3:49:58 AM
Long-term archiving on: : Thursday, January 9, 2020 - 10:18:31 PM

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

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

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