An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images
Joint Authors
Yang, Ming-Der
Yang, Yeh-Fen
Su, Tung-Ching
Huang, Kai-Siang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Genetic algorithm (GA) is designed to search the optimal solution via weeding out the worse gene strings based on a fitness function.
GA had demonstrated effectiveness in solving the problems of unsupervised image classification, one of the optimization problems in a large domain.
Many indices or hybrid algorithms as a fitness function in a GA classifier are built to improve the classification accuracy.
This paper proposes a new index, DBFCMI, by integrating two common indices, DBI and FCMI, in a GA classifier to improve the accuracy and robustness of classification.
For the purpose of testing and verifying DBFCMI, well-known indices such as DBI, FCMI, and PASI are employed as well for comparison.
A SPOT-5 satellite image in a partial watershed of Shihmen reservoir is adopted as the examined material for landuse classification.
As a result, DBFCMI acquires higher overall accuracy and robustness than the rest indices in unsupervised classification.
American Psychological Association (APA)
Yang, Ming-Der& Yang, Yeh-Fen& Su, Tung-Ching& Huang, Kai-Siang. 2014. An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1048962
Modern Language Association (MLA)
Yang, Ming-Der…[et al.]. An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1048962
American Medical Association (AMA)
Yang, Ming-Der& Yang, Yeh-Fen& Su, Tung-Ching& Huang, Kai-Siang. An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1048962
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048962