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An unsupervised approach for building change detection in VHR remote sensing imagery

Leichtle, Tobias und Geiß, Christian und Wurm, Michael und Martin, Klaus und Lakes, Tobia und Taubenböck, Hannes (2016) An unsupervised approach for building change detection in VHR remote sensing imagery. EARSeL Symposium 2016, 2016-06-20 - 2016-06-24, Bonn, Deutschland.

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Kurzfassung

Continuous monitoring of changes is one of the intrinsic capabilities of remote sensing. With respect to the increasing availability of very high resolution (VHR) remote sensing imagery, the capabilities become more and more relevant for rapidly changing complex urban environments. Therefore highly automatic concepts for analysis of changes are more and more required. In addition, appropriate unsupervised change detection approaches should be capable of handling VHR remote sensing data acquired by different sensors with possibly deviating viewing geometries and varying solar illumination angles. Especially concerning the high level of detail present in VHR imagery over urban areas, object-based methods facilitate change detection in this context. Another asset of the object-based analysis is that it inherently tackles discrepancies in exact spatial, spectral and radiometric matching of VHR image pairs. The aim of this paper is to present a novel object-based approach for unsupervised change detection with focus on individual buildings. The object-based paradigm allows the characterization of image objects by a large number of features that can be derived from the multi-temporal VHR image pairs. Modern VHR space-borne sensors like QuickBird, GeoEye, WorldView or Pléiades offer at least four multispectral image channels at spatial resolutions of approximately 50 centimeters. Different groups of features (e.g. 1st and 2nd order statistics of image channels) are compared regarding their discriminative power for building change detection. Principal component analysis is used as a feature extraction technique which compensates redundancies among features and enables proper data representation in the multi-dimensional feature space. For discrimination of changed and unchanged buildings, a comprehensive number of clustering algorithms from different methodological categories are evaluated regarding their capability of handling this two-class change detection problem. Overall, the proposed approach returned viable results which show the general suitability of clustering for object-based change detection. In detail, highest consistent accuracies were achieved using the algorithms k-means, partitioning around medoids, genetic k-means and the self-organizing map (SOM) clustering technique. We conclude that the proposed approach offers new benefits for building change detection particularly in rapidly changing urban settings, such as in Chinese cities.

elib-URL des Eintrags:https://elib.dlr.de/106275/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:An unsupervised approach for building change detection in VHR remote sensing imagery
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Leichtle, Tobiastobias.leichtle (at) slu-web.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Geiß, ChristianChristian.Geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553NICHT SPEZIFIZIERT
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894NICHT SPEZIFIZIERT
Martin, Klausklaus.martin (at) slu-web.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Lakes, Tobiatobia.lakes (at) geo.hu-berlin.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Taubenböck, HannesHannes.Taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126NICHT SPEZIFIZIERT
Datum:22 Juni 2016
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:change detection; clustering; object-based image analysis; unsupervised learning; very-high resolution (VHR) remote sensing
Veranstaltungstitel:EARSeL Symposium 2016
Veranstaltungsort:Bonn, Deutschland
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:20 Juni 2016
Veranstaltungsende:24 Juni 2016
Veranstalter :EARSeL
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Zivile Kriseninformation und Georisiken (alt), R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Leichtle, Tobias
Hinterlegt am:12 Okt 2016 10:19
Letzte Änderung:24 Apr 2024 20:11

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