摘要
针对利用Yamaguchi分解模型的四个散射分量直接进行类别归属判断精度不高并且所分类别有限的问题,结合模糊C均值的理论,提出了一种基于Yamaguchi分解模型的全极化SAR分类算法,把四个散射分量组成一组归一化的特征矢量,进行FCM聚类分析。并且用日本机载L波段PiSAR数据验证了该算法具有较高的分类精度和较好的视觉效果。
Aiming at overcoming the disadvantages of the limitation of class number and misclassification while classifying terrain and land directly by using four components decomposed by Yamaguchi target decomposition method from PolSAR images,a novel unsupervised classification algorithm for full polarimetric SAR images based on four-components scattering model and combined FCM theory is proposed and is applied to deal with L-band PiSAR image.This algorithm uses the four components as input features of FCM cluster.The experimental result demonstrates the accuracy and effectiveness of this algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第36期5-7,85,共4页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)No.2006AA12Z132
湖南省教育厅资助科研项目No.09C567~~
关键词
目标分解
四分量散射模型
模糊C均值
极化SAR分类
target decomposition
four-components scattering model
Fuzzy C-Means（FCM）
polarimetric SAR classification
