An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images
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 hy...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/264512 |
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| author | Ming-Der Yang Yeh-Fen Yang Tung-Ching Su Kai-Siang Huang |
| author_facet | Ming-Der Yang Yeh-Fen Yang Tung-Ching Su Kai-Siang Huang |
| author_sort | Ming-Der Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f4b154a357ed4521989ccf6a7a92ad6b |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-f4b154a357ed4521989ccf6a7a92ad6b2025-02-03T01:20:45ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/264512264512An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite ImagesMing-Der Yang0Yeh-Fen Yang1Tung-Ching Su2Kai-Siang Huang3Department of Civil Engineering, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Civil Engineering, National Chung Hsing University, Taichung 40227, TaiwanDepartment of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, TaiwanDepartment of Civil Engineering, National Chung Hsing University, Taichung 40227, TaiwanGenetic 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.http://dx.doi.org/10.1155/2014/264512 |
| spellingShingle | Ming-Der Yang Yeh-Fen Yang Tung-Ching Su Kai-Siang Huang An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images The Scientific World Journal |
| title | An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images |
| title_full | An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images |
| title_fullStr | An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images |
| title_full_unstemmed | An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images |
| title_short | An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images |
| title_sort | efficient fitness function in genetic algorithm classifier for landuse recognition on satellite images |
| url | http://dx.doi.org/10.1155/2014/264512 |
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