Hyperspectral datum classification using kernel method based on mutual information of neighbor bands  

作　　者：张淼 沈毅 王强 

机构地区：[1]Lab.of Detection Technology and Automatic Equipment,Dept.of Control Science and Engineering,Harbin Institute of Technology

出　　处：《Optoelectronics Letters》2009年第4期309-312,共4页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No.60604021)

摘　　要：Under the framework of support vector machines,this paper proposes a new kernel method based on neighbor bands mutual information for hyperspectral datum classification.This algorithm assigns weights to different bands in the kernel function according to the amount of useful information that they contain,which makes the band with more useful informa-tion play more important role in the classification.Our research has shown that the band with greater mutual information between neighbor bands contains more useful information,and hence we use the mutual information of each band and its neighbor bands as the weights of the proposed kernel method.The experimental results show that for the support vector machines based on polynomial and radial basis function,after introducing the proposed kernel function,the average accuracy is increased more than 1.2% without using any reference map or increasing much more computational time.Under the framework of support vector machines, this paper proposes a new kernel method based on neighbor bands mutual information for hyperspectral datum classification. This algorithm assigns weights to different bands in the kernel function according to the amount of useful information that they contain, which makes the band with more useful informa- tion play more important role in the classification. Our research has shown that the band with greater mutual information between neighbor bands contains more useful information, and hencewe use the mutual information of each band and its neighbor bands as the weights of the proposed kernel method. The experimental results show that for the support vector machines based on polynomial and radial basis function, after introducing the proposed kernel function, the average accuracy is increased more than 1.2% without using any reference map or increasing much more computational time.

关 键 词：分类方法 互信息 高光谱 邻居 内核 带基 基准 支持向量机 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]