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A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

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

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

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/68073/
Dokumentart:Zeitschriftenbeitrag
Titel:A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Sirmacek, BerilBeril.Sirmacek (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Unsalan, Cemunsalan (at) yeditepe.edu.trNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:1 Januar 2011
Erschienen in:IEEE Transactions on Geoscience and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:49
Seitenbereich:Seiten 211-221
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Ruf, ChristopherNICHT SPEZIFIZIERTNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Verlag:IEEE - Institute of Electrical and Electronics Engineers
Name der Reihe:Transactions on Geoscience and Remote Sensing
ISSN:0196-2892
Status:veröffentlicht
Stichwörter:Aerial images , Ikonos satellite images , building detection , data fusion , decision fusion , kernel density estimation , local feature vectors
HGF - Forschungsbereich:Verkehr und Weltraum (alt)
HGF - Programm:Weltraum (alt)
HGF - Programmthema:W EO - Erdbeobachtung
DLR - Schwerpunkt:Weltraum
DLR - Forschungsgebiet:W EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):W - Vorhaben Photogrammetrie und Bildanalyse (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Sirmacek, Beril
Hinterlegt am:07 Jan 2011 09:17
Letzte Änderung:31 Jul 2019 19:30

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