Elles représentent également une bonne alternative pour des situations de changements de capteurs. En effet, nous avons écarté cette problématique au profit d’une exploitation continue d’un seul capteur, néanmoins cette situation se pose dès que le satellite arrive en fin de vie, ou que l’on cherche à traiter des images d’archives. En effet, les utilisateurs optent de plus en plus pour la classification d’images passées afin d’étudier l’évo-lution de l’OCSsur le long terme. Ces problématiques vont demander la mise en correspondance entre des données d’anciens et de récents satellites. Les approches d’adaptation de domaine et de transfert d’appren-tissage constituent une des meilleures alternatives potentielles à ces problématiques. Les systèmes de fusion de classifieurs représentent une grande part de ces travaux de thèse. Néanmoins, nous avons choisi d’interrompre la prospection de certaines méthodes, tant la diversité des configurations possibles est vaste. En effet, sur le principe des votes majoritaires pondérés, le choix de la pondération à lui seul regroupe de nombreuses alternatives. Nous pouvons par exemple, ajouter une pondération par le FScore par classe, à l’image de la fusion Dempster-Shafer. Un autre exemple serait d’exploiter une mesure de similarité entre les domaines Source et Cible, qui devient alors une pondération de la décision de chaque classifieur. L’utilisation de l’inférence bayésienne ouvre la porte à toutes ces possibilités. En effet, toutes les informa-tions pouvant être traduites sous formes de probabilités devient un facteur potentiellement exploitable. Enfin, de nombreuses pistes d’améliorations concernent laMT. En effet, l’utilisation de contraintes plus fortes, associées à la capacité d’appréhender des systèmes complexes comme la rotation des cultures per-mettraient d’améliorer l’approche de mise à jour de la carte. 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