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Communication Dans Un Congrès Année : 2019

Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

Résumé

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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Dates et versions

hal-02276549 , version 1 (02-09-2019)

Identifiants

Citer

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. pp.4869-4872, ⟨10.1109/IGARSS.2019.8899851⟩. ⟨hal-02276549⟩
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