Besides , we study an unsupervised classification method based on polar decomposition 我们接着研究了基于极化分解的非监督分类方法。
In this dissertation , we study an unsupervised classification method based on fuzzy set theory 我们首先研究了基于模糊集理论的非监督分类方法。
This method is a combination of the usage of polarimetric information of polsar data and the unsupervised classification method based on fuzzy set theory 该方法是原始sar数据极化信息的利用和基于模糊集理论的非监督分类方法的结合。
The experimental results show that these two classification methods of multi - sources information fusion can result in better accuracy than that of conventional unsupervised classification method 实验结果表明基于bdset和fdset融合的分类方法比传统的非监督分类方法具有更好的分类效果,有效地提高了分类的精度。