An efficient fitness function in genetic algorithm classifier for landuse recognition on satellite images

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Abstract

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. © 2014 Ming-Der Yang et al.

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Yang, M. D., Yang, Y. F., Su, T. C., & Huang, K. S. (2014). An efficient fitness function in genetic algorithm classifier for landuse recognition on satellite images. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/264512

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