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《Science of Surveying and Mapping》 2011-02
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Remote sensing image classification based on Chaos Genetic algorithm

HUANG Ming①,WU Yan-bin②(①State Key Laboratory of Coal Resources and Safe Mining,CUM TB,Beijing 100083,China;②Deptement of Surveying and Mapping,Heilongjiang Institute of Technology,Harbin 150050,China)  
To improve the accuracy of remote sensing image classification,the Chaos Genetic algorithm was proposed in the paper.The Chaos Genetic algorithm has capability of self-learning,hence through the input samples the global optimization clustering center was found.And then the clustering center was employed to classify the view picture of remote sensing image.In this process,the ergodic property of Chaos phenomenon was used to optimize the initial population,so it could accelerate the convergence of Genetic algorithms.Chaotic systems are sensitive to initial condition system parameters.In order to escape from local optimums,the Chaos operator was applied to optimize the individuals after the process of selection operator,mutation operator and crossover operator.Chaos genetic algorithm was applied to classify the TM image of Huainan.Moreover,the classification of the Parallelepiped and Maximum likelihood and Standard Genetic algorithm methods were contrasted with it through confusion matrix.It was demonstrated that Chaos Genetic algorithm was superior to the two traditional algorithms,and its overall accuracy and Kappa coefficient reached 88.26% and 0.853 respectively.
【Fund】: 国家“973”项目(2006CB202209);; 国家自然基金重大项目(50490271)
【CateGory Index】: TP751
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