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Conference Papers Year : 2024

A multiresolution fusion framework based on probabilistic graphical modeling for burnt zones mapping from satellite and UAV imagery

Abstract

This paper tackles the semantic segmentation of zones affected by forest fires by the introduction of methods fusing multimodal imagery collected from unmanned aerial vehicles (UAVs) and satellite platforms. The multiresolution fusion task is especially challenging in this case because the difference between the involved spatial resolutions is very large -- a situation that is normally not addressed by traditional multiresolution schemes. Two novel multiresolution fusion approaches, based on Bayesian and probabilistic graphical fusion models and integrated with a deep fully convolutional network and with the expectation-maximization algorithm, are proposed. The application is to a real case of fire zone mapping and management in the area of Marseille, France.
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Dates and versions

hal-04584685 , version 1 (23-05-2024)

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  • HAL Id : hal-04584685 , version 1

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Martina Pastorino, Gabriele Moser, Fabien Guerra, Sebastiano B Serpico, Josiane Zerubia. A multiresolution fusion framework based on probabilistic graphical modeling for burnt zones mapping from satellite and UAV imagery. IEEE IGARSS 2024 - International Geoscience and Remote Sensing Symposium, IEEE, Jul 2024, Athenes, Greece. ⟨hal-04584685⟩
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