Domain adaptation with Hidden Markov Random Fields

TitleDomain adaptation with Hidden Markov Random Fields
Publication TypeConference Paper
Year of Publication2013
AuthorsJacobs, J-P., G. Thoonen, D. Tuia, G. Camps-Valls, B. Haest, and P. Scheunders
Conference NameGeoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Date PublishedJuly
Conference LocationMelbourne, VIC, Australia
Keywordsdomain adaptation, graph matching, Hidden Markov Random Fields, Multitemporal classification

In this paper, we propose a method to match multitemporal sequences of hyperspectral images using Hidden Markov Random Fields. Based on the matching of the data manifold, the algorithm matches the reflectance spectra of the classes, thus allowing the reuse of labeled examples acquired on one image to classify the other. This allows valorization of spectra collected in situ to other acquisitions than the one they were acquired for, without user supervision, prior knowledge of the class reflectance in the new domain or global information about atmospheric conditions.

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