线性判别分析是一种特征提取和维数缩减的方法，广泛应用于人脸识别，语音识别和手写字母识别等领域。但是许多线性判别分析都是“硬”线性判别分析，每个数据点都严格地属于这一类或那一类。在非相关判别转换(UDT)基础上，提出了模糊非相关判别转换(FUDT)。FUDT是利用模糊集理论的有监督学习方法，其判别向量满足广义瑞利商方程，同时也满足样本到模糊非相关优化判别向量上的投影是非相关的。通过FUDT和UDT对公共数据库MSTAR的实验结果可看出，FUDT在处理SAR图像的特征提取方面优于UDT。

Linear discriminant analysis is a way of feature extraction and dimension reduction. It is widely applied in face recognition, speech recognition, and handwriting recognition etc. However, many linear discriminant analyses are "hard" ones and every data point belongs to one class or another class strictly. In this paper, a fuzzy uncorrelated discriminant transformation (FUDT) is proposed based on uncorrelated discriminant transformation (UDT). FUDT is a supervised learning method with fuzzy set and its discriminant vectors satisfy the equation of generalized rayleigh quotient. Furthermore, the projection of samples to fuzzy uncorrelated optimal discriminant vectors is uncorrelated by FUDT. The experimental results show that FUDT is better than UDT in extracting the feature of SAR images which come from MSTAR.