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

Leichtle, Tobias and Geiß, Christian and Wurm, Michael and Martin, Klaus and Lakes, Tobia and Taubenböck, Hannes (2017) An unsupervised approach for building change detection in VHR remote sensing imagery. WorldView Global Alliance User Conference 2017, 2017-10-09 - 2017-10-11, München, Deutschland.

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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, these capabilities become more and more relevant for rapidly changing complex urban environments. Therefore highly automated 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 with 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 presentation 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 e.g., WorldView offer up to eight multispectral image channels at spatial resolution of about 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 unsupervised discrimination of changed and unchanged buildings, clustering has proven to be an eligible methodology. Overall, the proposed approach returned viable results which underline its general suitability 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.

Item URL in elib:https://elib.dlr.de/114638/
Document Type:Conference or Workshop Item (Speech)
Title:An unsupervised approach for building change detection in VHR remote sensing imagery
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Geiß, ChristianUNSPECIFIEDhttps://orcid.org/0000-0002-7961-8553UNSPECIFIED
Wurm, MichaelUNSPECIFIEDhttps://orcid.org/0000-0001-5967-1894UNSPECIFIED
Taubenböck, HannesUNSPECIFIEDhttps://orcid.org/0000-0003-4360-9126UNSPECIFIED
Date:11 October 2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:change detection, clustering, object-based image analysis, unsupervised learning, very-high resolution (VHR) remote sensing
Event Title:WorldView Global Alliance User Conference 2017
Event Location:München, Deutschland
Event Type:international Conference
Event Start Date:9 October 2017
Event End Date:11 October 2017
Organizer:European Space Imaging
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
German Remote Sensing Data Center > Land Surface
Deposited By: Leichtle, Tobias
Deposited On:19 Oct 2017 11:58
Last Modified:24 Apr 2024 20:19

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