NDLI: Segmentation and decision fusion for building detection

Content Provider	IEEE Xplore Digital Library
Author	Karadag, O.O. Senaras, C. Yarman Vural, F.T.
Copyright Year	2014
Description	Author affiliation: Middle East Tech. Univ., Ankara, Turkey (Karadag, O.O.; Yarman Vural, F.T.) \|\| Havelsan A.S., Ankara, Turkey (Senaras, C.)
Abstract	Segment based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Most of these studies includes generic segmentation methods and it is assumed that the segmentation output is compatible with the subsequent classification method. However, depending on the problem domain the properties of the regions such as size, shape etc. which are suitable for classification may vary. In this study, we propose a domain specific segmentation method for building detection. The contribution of the domain specific segmentation is empirically analyzed. For this purpose, first the decision fusion method is employed for building detection on the outputs of the state of the art segmentation methods, then it is employed on the output of the domain specific segmentation method and the classification performances for each method are compared. The advantage of domain specific segmentation is observed quantitatively and satisfactory results are obtained.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1604
Ending Page	1607
File Size	1336794
Page Count	4
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6946753
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Buildings Image segmentation Feature extraction Shape Image color analysis Vegetation mapping Markov random fields domain specific segmentation Segmentation building detection fusion
Content Type	Text
Resource Type	Article

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