Combination of supervised learning and unsupervised learning based on object association for land cover classification

Abstract : Conventional supervised classification approaches have significant limitations in the land cover classification from remote sensing data because a large amount of high quality labeled samples are difficult to guarantee. To overcome this limitation, combination with unsupervised approach is considered as one promising candidate. In this paper, we propose a novel framework to achieve the combination through object association based on Dempster-Shafer theory. Inspired by object association, the framework can label the unsupervised clusters according to the supervised classes even though they have different numbers. The proposed framework has been tested on the different combinations of commonly used supervised and unsupervised methods. Compared with the supervise methods, our proposed framework can furthest enhance the overall accuracy approximately by 8.2%. The experiment results proved that our proposed framework has achieved twofold performance gain: better performance on the insufficient training data case and the possibility to apply on a large area.
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Communication dans un congrès
DICTA2018, Dec 2018, Canberra, Australia
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https://hal.archives-ouvertes.fr/hal-01922096
Contributeur : Na Li <>
Soumis le : mercredi 14 novembre 2018 - 12:05:02
Dernière modification le : mardi 20 novembre 2018 - 01:02:19

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  • HAL Id : hal-01922096, version 1

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Na Li, Arnaud Martin, Rémi Estival. Combination of supervised learning and unsupervised learning based on object association for land cover classification. DICTA2018, Dec 2018, Canberra, Australia. 〈hal-01922096〉

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