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Change Detection by Fusion/Contextual Classification based on a Hybrid DSmT Model and ICM with Constraints

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 8
Year of Publication: 2011
Authors:
Azeddine Elhassouny
Soufiane Idbraim
Aissam Bekkari
Driss Mammass
Danielle Ducrot
10.5120/4422-6154

Azeddine Elhassouny, Soufiane Idbraim, Aissam Bekkari, Driss Mammass and Danielle Ducrot. Article: Change Detection by Fusion/Contextual Classification based on a Hybrid DSmT Model and ICM with Constraints. International Journal of Computer Applications 35(8):28-40, December 2011. Full text available. BibTeX

@article{key:article,
	author = {Azeddine Elhassouny and Soufiane Idbraim and Aissam Bekkari and Driss Mammass and Danielle Ducrot},
	title = {Article: Change Detection by Fusion/Contextual Classification based on a Hybrid DSmT Model and ICM with Constraints},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {35},
	number = {8},
	pages = {28-40},
	month = {December},
	note = {Full text available}
}

Abstract

The Dezert - Smarandache Theory (DSmT) used for the fusion and the modeling of the classes sets of themes has shown its performances in the detection and the cartography of the changes. Moreover the contextual classification with the research for the optimal solution by an ICM (Iterated conditional mode) algorithm with constraints allows to take in account the parcellary aspect of the thematic classes, thus, the introduction of this contextual information in the fusion process has enabled us to better identify the topics of surface and the detection of the changes.

The objective of this work is, in the first place, the integration in a fusion process using hybrid DSmT model, both, the contextual information obtained from a supervised ICM classification with constraints and the temporal information with the use of two images taken at two different dates. Secondly, we have proposed a new decision rule based on the DSmP transformation to overcome the inherent limitations of the decision rules thus use the maximum of generalized belief functions. The approach is evaluated on two LANDSAT ETM+ images, the results are promising.

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