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Reseach Article

Markov Radom Field Modeling for Fusion and Classification of Multisource Remotely Sensed Images

Published on September 2012 by Radja Kheddam, Aichouche Belhadj-aissa
Software Engineering, Databases and Expert Systems
Foundation of Computer Science USA
SEDEX - Number 2
September 2012
Authors: Radja Kheddam, Aichouche Belhadj-aissa
fc769e60-ecae-4002-8716-beae9ad5429f

Radja Kheddam, Aichouche Belhadj-aissa . Markov Radom Field Modeling for Fusion and Classification of Multisource Remotely Sensed Images. Software Engineering, Databases and Expert Systems. SEDEX, 2 (September 2012), 6-11.

@article{
author = { Radja Kheddam, Aichouche Belhadj-aissa },
title = { Markov Radom Field Modeling for Fusion and Classification of Multisource Remotely Sensed Images },
journal = { Software Engineering, Databases and Expert Systems },
issue_date = { September 2012 },
volume = { SEDEX },
number = { 2 },
month = { September },
year = { 2012 },
issn = 0975-8887,
pages = { 6-11 },
numpages = 6,
url = { /specialissues/sedex/number2/8360-1011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Software Engineering, Databases and Expert Systems
%A Radja Kheddam
%A Aichouche Belhadj-aissa
%T Markov Radom Field Modeling for Fusion and Classification of Multisource Remotely Sensed Images
%J Software Engineering, Databases and Expert Systems
%@ 0975-8887
%V SEDEX
%N 2
%P 6-11
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper, we discuss a Markov Random Field (MR) modeling for multisource and multitemporal remotely sensed image fusion and classification. Satellite images provided by individual sensor are incomplete, inconsistent or imprecise. Additional sources may provide complementary information and the fusion of multisource data can create a more consistent interpretation of the scene in which the associated uncertainty is decreased and the reliability of analysis results is increased. Also, a temporal data from a single sensor can be considered as separate information sources. The combination of multitemporal data over the same scene enhances information on changes that might have occurred in the area observed over time. Using these available data through a fusion and classification process, our objective is to extract more information to achieve greater accuracy in assigning pixels to thematic classes. The best methodological framework which allows the realization of this process is a Markov Random Field (MRF).

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Index Terms

Computer Science
Information Sciences

Keywords

Markov Random Field (mrf) Satellite Images Fusion Classification