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 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.
Department of Civil Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Ming-Der
0000-0003-2904-5838
MD
Yang
Department of Civil Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Yeh-Fen
YF
Yang
Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan.
Tung-Ching
TC
Su
Department of Civil Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Kai-Siang
KS
Huang
D000465:Algorithms; D062305:Geographic Mapping; D006801:Humans; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards*; D018531:Spacecraft / Q000592:standards*