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Information fusion approach for the data classification: an example for ERS-1/2 InSAR data

Palubinskas, Gintautas and Datcu, Mihai (2008) Information fusion approach for the data classification: an example for ERS-1/2 InSAR data. International Journal of Remote Sensing, 29 (16), pp. 4689-4703. Taylor & Francis. DOI: 10.1080/01431160801950154. ISSN 0143-1161.

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A huge amount of various remote sensing data have been acquired and archived during the recent years. Information extraction from these data is still a challenging task, for example using the data classification. We propose the Bayesian approach to image classification using information fusion from different sources of data. The method of classification is based on the three processing steps: (1) information fission by feature extraction, (2) data and dimensionality reduction by unsupervised clustering and (3) supervised classification with information fusion. The potential of the classification method is illustrated by the examples on ERS-1/2 Tandem interferometric synthetic aperture radar data. The continuity of tandem pairs of SAR images is ensured by already started or future missions such as TerraSAR-X, TanDEM-X and COSMO-SkyMed.

Document Type:Article
Title:Information fusion approach for the data classification: an example for ERS-1/2 InSAR data
AuthorsInstitution or Email of Authors
Palubinskas, GintautasUNSPECIFIED
Date:20 August 2008
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
In ISI Web of Science:Yes
Page Range:pp. 4689-4703
Remote Sensing and Photogrammetry Society, UNSPECIFIED
Publisher:Taylor & Francis
Keywords:feature extraction, clustering, classification, Bayesian inference, SAR
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben Bildwissenschaften (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute
Deposited By: Dr.math. Gintautas Palubinskas
Deposited On:24 Jul 2008
Last Modified:27 Apr 2009 14:08

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