Improvement of Land Cover Map from Satellite Imagery using DST and DSmT
Khedam, R.
Bouakache, A.
Mercier, G.
Belhadj-Aissa, A.
Fac. of Electron. & Comput. Sci., Univ. of Sci. & Technol. Houari Boumediene, Algiers;
This paper appears in: Information and Communication Technologies, 2006. ICTTA '06. 2nd
Publication Date: 0-0 0
Volume: 1,
On page(s): 383-388
Location: Damascus,
ISBN: 0-7803-9521-2
INSPEC Accession Number: 9061547
Digital Object Identifier: 10.1109/ICTTA.2006.1684400
Current Version Published: 2006-10-16
Abstract
The aim of this paper is to show that Dempster-Shafer theory (DST) and a recent theory of plausible and paradoxical reasoning introduced by Dezert and Smaradache and thus called Dezert-Smarandache theory (DSmT), can be successfully applied to improve a supervised classification of remotely sensed data. Notice that application fields of these two theories are related on multisensor/multitemporal/multiscale data fusion. In this study, our contribution lies in developing a new multispectral data classification process which can be seen as a multisensor fusion process where each thematic class is considered as one source of information
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