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Object oriented image analysis based on multi-agent recognition system

Mahmoudi, Fatemeh and Samadzadegan, Farhad and Reinartz, Peter (2013) Object oriented image analysis based on multi-agent recognition system. Computers & Geosciences, 54 (1), pp. 219-230. Elsevier. DOI: 10.1016/j.cageo.2012.12.007 ISSN 0098-3004

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Official URL: http://www.journals.elsevier.com/computers-and-geosciences/


In this paper, the capabilities of multi-agent systems are used in order to solve object recognition difficulties in complex urban areas based on the characteristics of WorldView-2 satellite imagery and digital surface model (DSM). The proposed methodology has three main steps: pre-processing of dataset, object based image analysis and multi-agent object recognition. Classified regions obtained from object based image analysis are used as input datasets in the proposed multi-agent system in order to modify/improve results. In the first operational level of the proposed multi-agent system, various kinds of object recognition agents modify initial classified regions based on their spectral, textural and 3D structural knowledge. Then, in the second operational level, 2D structural knowledge and contextual relations are used by agents for reasoning and modification. Evaluation of the capabilities of the proposed object recognition methodology is performed on WorldView-2 imagery over Rio de Janeiro (Brazil) which has been collected in January 2010. According to the obtained results of the object based image analysis process, contextual relations and structural descriptors have high potential to modify general difficulties of object recognition. Using knowledge based reasoning and cooperative capabilities of agents in the proposed multi-agent system in this paper, most of the remaining difficulties are decreased and the accuracy of object based image analysis results is improved for about three percent.

Item URL in elib:https://elib.dlr.de/81661/
Document Type:Article
Title:Object oriented image analysis based on multi-agent recognition system
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mahmoudi, FatemehUniverität TeheranUNSPECIFIED
Samadzadegan, Farhadfarhad.samadzadegan (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:March 2013
Journal or Publication Title:Computers & Geosciences
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.cageo.2012.12.007
Page Range:pp. 219-230
Keywords:Object recognition Multi-agent Texture Contextual information Structural descriptor
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Reinartz, Prof. Dr.. Peter
Deposited On:15 Mar 2013 14:16
Last Modified:12 Dec 2013 22:02

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