文章基本信息

标题：Hyperspectral Image Feature Extraction Based on Generalized Discriminant Analysis
本地全文：下载
作者：Guopeng Yang ; Xuchu Yu ; Xin Zhou 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：285-290
出版社：Copernicus Publications
摘要：The hyperspectral image enriches spectrum information, so compared with panchromatic image and multispectral image; it can classify the ground target better. The feature extraction of hyperspectral image is the necessary step of the ground target classification, and the kernel method is a new way to extract the nonlinear feature. In this paper, First the mathematical model of the generalized discriminant analysis was described, and then the processing method of this model was given, finally, we did two experiments. Through the tests, we can see that, in the feature space extracted by generalized discriminant analysis, the samples of the same class are near with each other; the samples of the different classes are far away. It can be concluded that the method described in this paper is suitable to hyperspectral image classification, and it can do better job than the method of linear discriminant analysis
关键词：Hyperspectral Image; Feature Extraction; Generalized Discriminant Analysis; Kernel Function