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Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

Andrew Khalel 1, 2 Onur Tasar 1 Guillaume Charpiat 3 Yuliya Tarabalka 1 
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
3 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : In this work, we propose a novel multi-task framework, to learn satellite image pansharpening and segmentation jointly. Our framework is based on the encoder-decoder architecture, where both tasks share the same encoder but each one has its own decoder. We compare our framework against single-task models with different architectures. Results show that our framework outperforms all other approaches in both tasks.
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Submitted on : Monday, September 2, 2019 - 5:28:29 PM
Last modification on : Saturday, June 25, 2022 - 11:39:45 PM
Long-term archiving on: : Wednesday, February 5, 2020 - 11:27:24 AM


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  • HAL Id : hal-02276549, version 1


Andrew Khalel, Onur Tasar, Guillaume Charpiat, Yuliya Tarabalka. Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation. IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨hal-02276549⟩



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