Domain adaptation with Hidden Markov Random Fields

Publication Type:

Conference Paper


Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, Melbourne, VIC, Australia, p.3112-3115 (2013)


domain 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.

Research area: