Publication
Title
Domain adaptation with hidden Markov random fields
Author
Abstract
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.
Language
English
Source (journal)
IEEE International Geoscience and Remote Sensing Symposium proceedings. - [New York]
Source (book)
IEEE IGARSS 2013, International Geoscience and Remote Sensing Symposium , Melbourne, Australia, July 21-26
Publication
S.l. : Institute of Electrical and Electronics Engineers, 2013
ISSN
2153-6996
ISBN
978-1-4799-1114-1
Volume/pages
p. 3112-3115
ISI
000345638903046
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 17.02.2014
Last edited 03.11.2017
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