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Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images

Qin, Rongjun and Tian, Jiaojiao and Reinartz, Peter (2016) Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images. International Journal of Remote Sensing, 37 (15), pp. 3455-3476. Taylor & Francis. DOI: 10.1080/01431161.2015.1066527 ISSN 0143-1161

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Official URL: http://www.tandfonline.com/doi/full/10.1080/01431161.2015.1066527

Abstract

Automatic building detection from very-high-resolution (VHR) satellite images is a difficult task. The detection accuracy is usually limited by spectral ambiguities and the uncertainties of the available height information. Feature extraction and training sampling collection for supervised methods are other sources of uncertainty. Most widely used VHR sensors have shorter revisit cycles (IKONOS/GeoEye-1/2, 3 days; WorldView 1/2, 1.1 days) due to large off-nadir viewing angles and hence are able to perform consistent acquisition of mono or stereo images. In this article, we investigate the possibility of using high-temporal stereo VHR images to enhance remote-sensing image interpretation under the context of building detection. Digital surface models, which contain the height information, are generated for each date using semi-global matching. Pre-classification is performed combining the height and spectral information to obtain an initial building probability map. With a reference land cover map available for one date, the training samples of the other dates are automatically derived using a rule-based validating procedure. A spatiotemporal inference filter is developed considering the spectral, spatial, and temporal aspects to enhance the building probability maps. This aims at homogenizing the building probability values of spectrally similar pixels in the spatial domain and geometrically similar pixels in the temporal domain, while being robust to the silhouette of the images and geometric discrepancies of the multitemporal data. The effectiveness and robustness of the proposed method are evaluated by performing three experiments on six stereo pairs of the same region over a time period of five years (2006–2011). The area under curve (AUC) of the receiver operating characteristic and kappa statistic (κ) are employed to assess the results. These experiments show that spatiotemporal inference filtering largely improves the accuracy of the building probability map (average AUC = 0.95) while facilitating building extraction in snow-covered images. The resulting building probability maps can be further used for other applications (e.g. building footprint updating).

Item URL in elib:https://elib.dlr.de/97983/
Document Type:Article
Additional Information:ISSN: 0143-1161 (Print), 1366-5901 (Online)
Title:Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Qin, RongjunSingapore ETH Center, Future Cities Laboratory, SingaporeUNSPECIFIED
Tian, Jiaojiaojiaojiao.tian (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:2016
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:37
DOI :10.1080/01431161.2015.1066527
Page Range:pp. 3455-3476
Editors:
EditorsEmail
Warner, Timothy A.West Virginia University, USA
Publisher:Taylor & Francis
ISSN:0143-1161
Status:Published
Keywords:spatiotemporal, building detection
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Tian, Jiaojiao
Deposited On:08 Sep 2015 13:06
Last Modified:31 Jul 2019 19:54

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