遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例  

Wetland Remote Sensing Classification Using Support Vector Machine Optimized With Genetic Algorithm: A Case Study in Honghe Nature National Reserve

作　　者：臧淑英[1] 张策[1] 张丽娟[1] 张玉红[1] 

机构地区：[1]哈尔滨师范大学地理科学学院,黑龙江哈尔滨150025

出　　处：《地理科学》2012年第4期434-441,共8页Scientia Geographica Sinica

基　　金：国家自然基金重点项目(41030743)资助

摘　　要：湿地遥感分类作为湿地管理、监测与评价的重要手段,受到了广泛的关注。遗传算法(GA)借鉴了生物进化规律进行启发式搜索寻优,支持向量机(SVM)是一种新型的空间数据挖掘方法,二者相结合可以发挥各自的优势,寻找到支持向量机的全局最优参数,从而较准确地对湿地进行遥感分类。以洪河自然保护区为例,采用遗传算法优化的支持向量机方法进行了湿地遥感分类研究。同格网搜索下的支持向量机湿地遥感分类及最大似然监督分类对比,结果表明,遗传算法优化较格网搜索方式总精度提高了7.29%,较最大似然监督分类提高了12.06%,方法改善了沼泽、草地与裸地三种地物间的区分,是湿地遥感分类的有效手段。Wetland remote sensing classification,as an important means of wetland management,monitoring and assessment,has been widely concerned.Genetic Algorithm（GA） does heuristic search optimization which references the law of biological evolution,while Support Vector Machine（SVM） is a new kind of spatial data mining method.Combination of both can develop their own advantages to do wetland remote sensing classification exactly,by searching the global optimal parameters of Support Vector Machine.Taking Honghe Nature Reserve as a case study,wetland remote sensing classification using Support Vector Machine optimized with Genetic Algorithm（GA-SVM） was explored in this paper.In comparison with wetland classification using support vector machine with parameters searched by Grid and the Maximum Likelihood Classification.The experimental results show that,the overall accuracy of Genetic Algorithm optimization has increased 7.29% compared to Grid Search method,and has increased 12.06% compared to the Maximum Likelihood Classification,by improving the discrimination among marsh,meadow and bare land.Therefore,GA-SVM is an effective tool in wetland remote sensing classification.

关 键 词：湿地 遥感分类 遗传算法 支持向量机 洪河自然保护区 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]