Improvement of Land Cover Map from Satellite Imagery using DST and DSmT

Author

Khedam, Radja ; Bouakache, Abdenour ; Mercier, Gregoire ; Belhadj-Aissa, Aichouche

Author_Institution

Fac. of Electron. & Comput. Sci., Univ. of Sci. & Technol. Houari Boumediene, Algiers

Volume

1

fYear

0

fDate

0-0 0

Firstpage

383

Lastpage

388

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

Keywords

image classification; inference mechanisms; sensor fusion; Dempster-Shafer theory; Dezert-Smarandache theory; land cover map; multisensor data fusion; multispectral data classification; paradoxical reasoning; remotely data sensing; satellite imagery; supervised classification; Bayesian methods; Classification algorithms; Computer science; Image processing; Information resources; Laboratories; Mathematical model; Remote monitoring; Satellites; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Communication Technologies, 2006. ICTTA '06. 2nd

Conference_Location

Damascus

Print_ISBN

0-7803-9521-2

Type

conf

DOI

10.1109/ICTTA.2006.1684400

Filename

1684400