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An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images

By Ming-Der Yang, Yeh-Fen Yang, Tung-Ching Su and Kai-Siang Huang


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

Topics: Humans, Pattern Recognition, Automated, Spacecraft, Algorithms, Geographic Mapping, Models, Genetic
Year: 2015
DOI identifier: 10.1155/2014
OAI identifier:

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