Modified Fisher's Linear Discriminant Analysis for Hyperspectral Imagery
Modified Fisher's Linear Discriminant Analysis for Hyperspectral Imagery
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DOI：
10.1109/lgrs.2007.900751
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发表时间：
2007-10
影响因子：
4.8
通讯作者：
Q. Du
中科院分区：
文献类型：
--
作者：
Q. Du
在这封信中，我们提出了一种改进的费舍尔线性判别分析（MFLDA），用于高光谱遥感图像的降维。 Fisher线性判别分析（FLDA）的基本思想是设计一种最优变换，使类间与类内散布矩阵的比率最大化，从而使类在低维空间中能够很好地分离。将 FLDA 应用到高光谱图像的实际困难包括无法获得足够的训练样本以及所有类别的未知信息。因此对原有的FLDA进行修改，以避免训练样本和完整类知识的要求。 MFLDA 仅需要所需的类签名。使用 MFLDA 转换数据的分类结果表明，所需的类信息得到了很好的保留，并且可以在低维空间中轻松分离。
In this letter, we present a modified Fisher's linear discriminant analysis (MFLDA) for dimension reduction in hyperspectral remote sensing imagery. The basic idea of the Fisher's linear discriminant analysis (FLDA) is to design an optimal transform, which can maximize the ratio of between-class to within-class scatter matrices so that the classes can be well separated in the low-dimensional space. The practical difficulty of applying FLDA to hyperspectral images includes the unavailability of enough training samples and unknown information for all the classes present. So the original FLDA is modified to avoid the requirements of training samples and complete class knowledge. The MFLDA requires the desired class signatures only. The classification result using the MFLDA-transformed data shows that the desired class information is well preserved and they can be easily separated in the low-dimensional space.