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A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Sirmacek, Beril and Unsalan, Cem (2011) A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images. IEEE Transactions on Geoscience and Remote Sensing, 49 (1), pp. 211-221. IEEE - Institute of Electrical and Electronics Engineers. ISSN 0196-2892.


Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5523977


Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.

Item URL in elib:https://elib.dlr.de/68073/
Document Type:Article
Title:A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sirmacek, BerilBeril.Sirmacek (at) dlr.deUNSPECIFIED
Unsalan, Cemunsalan (at) yeditepe.edu.trUNSPECIFIED
Date:1 January 2011
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 211-221
EditorsEmailEditor's ORCID iD
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:Transactions on Geoscience and Remote Sensing
Keywords:Aerial images , Ikonos satellite images , building detection , data fusion , decision fusion , kernel density estimation , local feature vectors
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 Photogrammetrie und Bildanalyse (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Sirmacek, Beril
Deposited On:07 Jan 2011 09:17
Last Modified:31 Jul 2019 19:30

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