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Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment

Leichtle, Tobias and Geiß, Christian and Wurm, Michael and Lakes, Tobia and Taubenböck, Hannes (2017) Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, pp. 15-27. Elsevier. DOI: 10.1016/j.jag.2016.08.010 ISSN 0303-2434

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Official URL: http://dx.doi.org/10.1016/j.jag.2016.08.010

Abstract

Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of κ statistics of 0.8–0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to κ statistics, which underlines the robustness of the proposed approach.

Item URL in elib:https://elib.dlr.de/106056/
Document Type:Article
Title:Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Leichtle, Tobiastobias.leichtle (at) slu-web.deUNSPECIFIED
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
Wurm, Michaelmichael.wurm (at) dlr.deUNSPECIFIED
Lakes, Tobiatobia.lakes (at) geo.hu-berlin.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:February 2017
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:54
DOI :10.1016/j.jag.2016.08.010
Page Range:pp. 15-27
Publisher:Elsevier
ISSN:0303-2434
Status:Published
Keywords:Change detection; Object-based image analysis; Principal component analysis; Clustering; Very-high resolution (VHR) remote sensing; Urban environment
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 Zivile Kriseninformation und Georisiken (old), R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Leichtle, Tobias
Deposited On:19 Sep 2016 11:41
Last Modified:06 Sep 2019 15:16

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