基于相关向量机的高光谱影像分类研究

Research on Relevance Vector Machine for Hyperspectral Imagery Classification

从分析支持向量机用于高光谱影像分类时存在的不足出发,提出一种基于相关向量机的高光谱影像分类方法。在介绍稀疏贝叶斯分类模型的基础上,将相关向量机学习转化为最大化边缘似然函数参数估计问题,并采用快速序列稀疏贝叶斯学习算法。通过PHI和OMIS影像分类试验分析表明基于相关向量机的高光谱影像分类方法的优势。

Though the support vector machine has been successfully applied in hyperspectral imagery classification,it has also several limitations.Relevance vector machine（RVM） is a sparse model in the Bayesian framework,its mathematics model doesn＇t have regularization coefficient and its kernel functions don＇t need to satisfy Mercer＇s condition.RVM presents the good generalization performance,and its predictions are probabilistic.In this paper,we firstly analysis the disadvantages of the support vector machine for hyperspectral imagery classification,and then a hyperspectral imagery classification method based on the relevance machine is brought forward.We introduce the sparse Bayesian classification model,regard the RVM learning as the maximization of marginal likelihood,and select the fast sequential sparse Bayesian learning algorithm.Through the experiments of PHI and OMIS imageries,the advantages of the relevance machine used in hyperspectral imagery classification are given out.