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The rough sets feature selection for trees recognition in color aerial images using genetic algorithms

Pan, Li, Nahavandi, Saeid and Zheng, Hong 2003, The rough sets feature selection for trees recognition in color aerial images using genetic algorithms, in Program & abstracts : the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems : CIRAS 2003 : 15-18 December 2003, Pan Pacific Hotel, Singapore, Center for Intelligent Control, National University of Singapore, Singapore.

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Title The rough sets feature selection for trees recognition in color aerial images using genetic algorithms
Author(s) Pan, Li
Nahavandi, Saeid
Zheng, Hong
Conference name International Conference on Computational Intelligence, Robotics and Autonomous Systems (2nd : 2003 : Singapore)
Conference location Singapore
Conference dates 15-18 December 2003 2003
Title of proceedings Program & abstracts : the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems : CIRAS 2003 : 15-18 December 2003, Pan Pacific Hotel, Singapore
Editor(s) Vadakkepat, Prahlad
Publication date 2003
Publisher Center for Intelligent Control, National University of Singapore
Place of publication Singapore
Summary Selecting a set of features which is optimal for a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the relative reduct decision table of the rough set. This approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and finds the effective feature subset for texture classification . On the basis of the effective feature subset selected, this paper presents a method to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The experiments results show that the feature subset selected and the method of the object extraction presented in this paper are practical and effective.
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ISSN 0219-6131
Language eng
Field of Research 099999 Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003, CIRAS
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005248

Document type: Conference Paper
Collections: School of Engineering and Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.