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Position: Home > Articles > Land use classification based on chaos immune algorithm and remote sensing image Transactions of the Chinese Society of Agricultural Engineering 2007,23 (6) 154-158

基于混沌免疫算法和遥感影像的土地利用分类

作  者：
武彦斌;彭苏萍
单  位：
中国矿业大学(北京)煤炭资源与安全开采国家重点实验室;河北经贸大学工商管理学院
关键词：
遥感影像;混沌;免疫算法;土地利用分类
摘  要：
为提高利用遥感影像进行土地利用分类的精度,采用了基于混沌免疫算法(Chaos Immune Algorithm)的多光谱遥感影像分类方法。首先应用混沌免疫算法对样本进行自学习得到全局最优的聚类中心,然后通过得到的聚类中心对整幅影像进行分类。该方法利用混沌变量的遍历性,进行粗粒搜索,优化免疫算法的初始抗体群;通过克隆选择算子、变异算子、抗体的循环补充操作,避免陷入局部最优解,得到全局最优的聚类中心。在对淮南矿区采用TM影像进行的土地利用分类中,试验结果表明该方法分类总精度为89.9%,Kappa系数为0.873,优于传统的Parallelepiped和Maximum likelihood分类方法。证明通过混沌变量的遍历搜索一定程度上克服了样本光谱值的局部性,引入免疫机制可以改善解的最优性。
译  名：
Land use classification based on chaos immune algorithm and remote sensing image
作  者：
Wu Yanbin1,2,Peng Suping2 (1.School of Business Administration,Hebei University of Economics and Business,Shijiazhuang 050016,China;2.National Laboratory of Coal Resources and Mine Safety,China University of Minging and Technology,Beijing 100083,China)
关键词：
remote sensing image;chaos;immune algorithm;land use classification
摘  要：
To improve the accuracy of land use classification based on remote sensing image,Chaos Immune Algorithm was proposed.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 antibody population.Through the clone selection operator,mutation operator and recruited antibody,local optimums were avoided.Chaos Immune Algorithm was applied to classify land use in Huainan based on TM image.Based on confusion matrix,the landuse classification results of the Parallelepiped and Maximum likelihood methods were contrasted with Chaos Immune Algorithm.The results show that Chaos Immune Algorithm is superior to the two traditional algorithms,and its overall accuracy and Kappa coefficient reach 89.9% and 0.873,respectively.It is demonstrated that the ergodic property of chaos phenomenon can overcome data locality in samples and the immune algorithm can improve overall solution optimization.

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