%0 Journal Article
%T Hyperspectral image classification based on Monte Carlo feature reduction method<br>基于蒙特卡罗特征降维算法的小样本高光谱图像分类
%A ZHAO Chun-Hui
%A QI Bin
%A Eunseog Youn
%A <br>赵春晖
%A 齐滨
%A Eunseog Youn
%J 红外与毫米波学报
%D 2013
%I Science Press
%X Hyperspectral image classification is an important research aspect of hyperspectral data analysis. Relevance vector machine (RVM) is widely utilized since it is not restricted to Mercer condition and does not have to set the penalty factor. Due to the high dimension of hyperspectral data, the classification accuracy is severely affected when there are few training samples. Feature reduction is a common method to deal with this phenomenon. However, most of the filter model based feature selection methods can not provide optimal feature selection number. This paper proposes to utilize the statistic estimation characteristic of Monte Carlo random experiments to calculate optimal feature reduction number and conduct hyperspectral image classification with relevance vector machine. Experimental results show the reliability of the feature reduction number calculated by Monte Carlo method. Compared with the classification of original data, there is a significant improvement in the classification accuracy with the feature reduction data.
%K hyperspectral image processing
%K Mote Carlo feature reduction method
%K relevance vector machine
%K optimal feature reduction number<br>高光谱图像处理
%K 蒙特卡罗特征降维算法
%K 相关向量机
%K 最优降维波段数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=D3B4F771D1A06062008B4D0A2EF05996&aid=575970AF4D203263A4A5BBD6801D6B08&yid=FF7AA908D58E97FA&vid=9971A5E270697F23&iid=38B194292C032A66&sid=95D537AC89B28832&eid=5D71B28100102720&journal_id=1001-9014&journal_name=红外与毫米波学报&referenced_num=0&reference_num=0