NDLI: Building Detection in Aerial Images Based on Watershed and Visual Attention Feature Descriptors

Content Provider	IEEE Xplore Digital Library
Author	Cretu, A.-M. Payeur, P.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Univ. of Ottawa, Ottawa, ON, Canada (Payeur, P.) \|\| Dept. of Comput. Sci. & Eng., Univ. du Quebec en Outaouais, Gatineau, QC, Canada (Cretu, A.-M.)
Abstract	This paper investigates a novel solution for the recognition of objects of interest in aerial images. The solution builds on a combination of algorithms inspired from the human visual system with classical and modern algorithms. The goal is to achieve intelligent and powerful approaches that allow for fast and automatic treatment of complex images. The methodology that is proposed innovatively combines a variation of the classical watershed segmentation algorithm with a series of feature descriptors derived from a computational model of visual attention. The feature descriptors are tuned with a machine learning approach for the task of detecting buildings in aerial images. The experimental evaluation that is conducted demonstrates that objects recognition with features derived from human visual attention performs better than when only traditional features, such as statistical texture descriptors and shape descriptors, are used. As well, the proposed solution obtains better classification rates than those reported on image processing-based recognition of buildings in the remote sensing literature.
Starting Page	265
Ending Page	272
File Size	637717
Page Count	8
File Format	PDF
ISBN	9781467364096
e-ISBN	9780769549835
DOI	10.1109/CRV.2013.8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-28
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	visual attention object recognition Visualization Shape Buildings remote sensing Training Image segmentation aerial images feature descriptors Vegetation mapping Feature extraction segmentation
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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