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Classification accuracy increase using multisensor data fusion

Makarau, Aliaksei and Palubinskas, Gintautas and Reinartz, Peter (2011) Classification accuracy increase using multisensor data fusion. Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover. ISPRS Hannover Workshop 2011, June 14 - 17, 2011, Hannover, Germany. ISSN 1682-1777

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Abstract

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc.

Item URL in elib:https://elib.dlr.de/72755/
Document Type:Conference or Workshop Item (Paper, Poster)
Title:Classification accuracy increase using multisensor data fusion
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Makarau, Aliakseialiaksei.makarau (at) dlr.deUNSPECIFIED
Palubinskas, Gintautasgintautas.palubinskas (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:June 2011
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:XXXVII
Page Range:pp. 1-6
Editors:
EditorsEmail
Heipke, CUNSPECIFIED
Jacobsen, KUNSPECIFIED
Rottensteiner, FUNSPECIFIED
Müller, SUNSPECIFIED
Sörgel, UUNSPECIFIED
Publisher:Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover
Series Name:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN:1682-1777
Status:Published
Keywords:Multispectral image, VNIR data, WorldView-2, SAR, TerraSAR-X, data fusion, classification, very high resolution
Event Title:ISPRS Hannover Workshop 2011
Event Location:Hannover, Germany
Event Type:international Conference
Event Dates:June 14 - 17, 2011
Organizer:Institut für Photogrammetrie und GeoInformation (IPI), Leibniz Universität Hannover
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 hochauflösende Fernerkundungsverfahreen (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Makarau, Aliaksei
Deposited On:13 Dec 2011 08:00
Last Modified:31 Jul 2019 19:34

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