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Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series

Ihsen Hedhli 1 Gabriele Moser 2 Sebastiano Serpico 2 Josiane Zerubia 3, 4
1 ULaval - Université Laval [Québec]
2 DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture
3 AYANA - Télédetection et IA embarqués pour le ”New Space”
CRISAM - Inria Sophia Antipolis - Méditerranée
4 UCA - Université Côte d'Azur
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https://hal.inria.fr/hal-03466612
Contributor : Jules Mabon Connect in order to contact the contributor
Submitted on : Monday, December 6, 2021 - 9:22:11 AM
Last modification on : Friday, February 11, 2022 - 2:32:35 PM
Long-term archiving on: : Monday, March 7, 2022 - 6:15:15 PM

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Ihsen Hedhli, Gabriele Moser, Sebastiano Serpico, Josiane Zerubia. Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series. Abdourrahmane Atto; Francesca Bovolo; Lorenzo Bruzzone. Change Detection and Image Time Series Analysis, 2, ISTE-Wiley, pp.1-27, 2021, Supervised Methods, 9781789450576. ⟨hal-03466612⟩

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