摘要
为有效提高最小类内方差算法在遥感图像分割中的实时性,在分析最小类内方差算法和k-均值聚类算法原理的基础上,证明两者判别准则函数的等效性,利用k-均值聚类算法的高效性对最小类内方差算法进行优化。实验结果表明,优化的最小类内方差算法搜索空间小,获取阈值速度快,具有较强的实时性。
In order to reduce the computation of the minimum interclass variance algorithm in the remote sensing image segmentation,the equivalent of the objective functions is proved based on the principles of the minimum variance algorithm and the k-means clustering algorithm.In addition,a new fast minimum variance algorithm based on the k-means optimization is proposed.Experimental results show that it effectively reduces the hunting zone and has a real-time speed to calculate the optimal threshold
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第13期219-220,226,共3页
Computer Engineering
基金
科技部国际科技合作基金资助项目(2009DFA12870)
关键词
最小类内方差算法
K-均值聚类算法
遥感
图像分割
minimum interclass variance algorithm
k-means clustering algorithm
remote sensing
image segmentation
