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A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images

DSpace at IIT Bombay

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Title A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images
 
Creator COSSU, R
CHAUDHURI, S
BRUZZONE, L
 
Subject remote-sensing images
random-field approach
cascade-classifier
maximum-likelihood
algorithm
contextual classification
expectation-maximization (em) algorithm
markov random fields (mrfs)
partially supervised classification
partially supervised updating of land-cover maps
 
Description An advanced context-sensitive classification technique that exploits a temporal series of remote sensing images for a regular updating of land-cover maps is proposed. This technique extends the use of spatio-contextual information to the framework of partially supervised approaches (that are capable of addressing the updating problem under the realistic, though critical, constraint that no ground-truth information is available for some of the images to be classified). The proposed classifier is based on an iterative partially supervised algorithm that jointly estimates the class-conditional densities and the prior model for the class labels on the image to be classified by taking into account spatio-contextual information. Experimental results point out that the proposed technique is effective and that it significantly outperforms the context-insensitive partially supervised approaches presented in the literature.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2011-07-31T16:17:46Z
2011-12-26T12:53:04Z
2011-12-27T05:40:11Z
2011-07-31T16:17:46Z
2011-12-26T12:53:04Z
2011-12-27T05:40:11Z
2005
 
Type Article
 
Identifier IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2(3), 352-356
1545-598X
http://dx.doi.org/10.1109/LGRS.2005.851541
http://dspace.library.iitb.ac.in/xmlui/handle/10054/8166
http://hdl.handle.net/10054/8166
 
Language en