NDLI: Building detection using local features and DSM data

Content Provider	IEEE Xplore Digital Library
Author	Ozcan, A.H. Unsalan, C. Reinartz, P.
Copyright Year	2013
Description	Author affiliation: DLR, Wessling, Germany (Reinartz, P.) \|\| Yeditepe Univ., Istanbul, Turkey (Unsalan, C.) \|\| Tubitak BILGEM, Gebze-Kocaeli, Turkey (Ozcan, A.H.)
Abstract	Detecting and locating buildings in satellite images has various application areas. Unfortunately, manually detecting buildings is hard and very time consuming. Therefore, in the literature several methods are proposed to automatically detect buildings. These methods can be divided into two main groups. In the first group, researchers used panchromatic or multispectral information to detect buildings. In the second group, researchers used DSM data to detect buildings. In this study, we propose two novel methods to detect buildings by combining the panchromatic and DSM data. The first method uses corner points extracted by Harris corner detection method. These corner points are used jointly with DSM data. Using a kernel based density estimation method, possible building locations are detected. In the second method, shadow of buildings are used in a similar way. We tested both methods on WorldView-2 images and DSM data generated from them.
Sponsorship	IEEE Aerosp. Electron. Syst. Soc.
Starting Page	139
Ending Page	143
File Size	1621562
Page Count	5
File Format	PDF
ISBN	9781467363952
e-ISBN	9781467363969
DOI	10.1109/RAST.2013.6581188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-12
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Satellites Shape Buildings Estimation Feature extraction Building detection Data mining Kernel density estimation Kernel DSM
Content Type	Text
Resource Type	Article

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