DocumentCode :
1398490
Title :
A Novel Domain Adaptation Bayesian Classifier for Updating Land-Cover Maps With Class Differences in Source and Target Domains
Author :
Bahirat, Kanchan ; Bovolo, Francesca ; Bruzzone, Lorenzo ; Chaudhuri, Subhasis
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol. Bombay, Mumbai, India
Volume :
50
Issue :
7
fYear :
2012
fDate :
7/1/2012 12:00:00 AM
Firstpage :
2810
Lastpage :
2826
Abstract :
This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.
Keywords :
Bayes methods; geophysical image processing; image classification; remote sensing; DA Bayesian classifier; DA-MAP classifier; class differences; domain adaptation Bayesian classifier; ground reference information; land cover maps; multitemporal acquisition; multitemporal remote sensing images classification; source domain; target domain; Bayesian methods; Computer aided software engineering; Estimation; Reliability; Remote sensing; Training; Vectors; Bayesian classifier; domain adaptation (DA); land-cover map updating; maximum a posteriori (MAP) classifier; multitemporal image classification; partially supervised learning; partially unsupervised learning; remote sensing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2011.2174154
Filename :
6104136
Link To Document :
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