摘要
针对遥感信息的不确定性和混合像元问题,分析FCM算法。为了避免FCM初值选取不当而陷入局部最优,提出基于基因表达式编程的遥感数字图像模糊聚类算法。该算法可以利用外层GEP算法的全局寻优能力,确定最佳初始聚类中心,再利用内层FCM算法的模糊聚类和局部快速收敛的特性获得遥感数字图像的最优聚类。
The accuracy of traditional classification method based on remote sensing image is difficult to achieve practical requirements because of remote sensing information uncertainty and the existence of mixed pixel.So Fuzzy C-Means clustering method is analyzed and realized.The researches show the behavior of the FCM clustering depends on the quality of the initialization of the parameters strongly.Remote Sensing Digital Image Fuzzy Clustering based on Gene Expression Programming（RSDIFC-GEP） is proposed and realized.By incorporating the local and global search and taking the clustering result of GEP as the initialized value of the FCM,the algorithm eliminates FCM trapped local optimum and is sensitive to initial value effectively.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第10期199-200,238,共3页
Computer Engineering
基金
国家自然科学基金资助项目"基因表达式编程的机理及高效算法研究"(60763012)
广西高等学校优秀人才资助计划基金资助项目"基于GEP知识发现的机理及算法研究"(RC2007022)
关键词
基因表达式编程
遥感
数字图像
模糊聚类
Gene Expression Programming（GEP）
remote sensing
digital image
fuzzy clustering
