基于相关向量机的高光谱图像解混方法  

An unmixing algorithm based on the relevance vector machine for hyperspectral imagery

作　　者：杨京辉[1] 王立国[1] 钱晋希[2,3] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001 [2]中国空间技术研究院通信卫星事业部,北京100094 [3]北京邮电大学电子工程学院,北京100876

出　　处：《哈尔滨工程大学学报》2015年第2期267-270,286,共5页Journal of Harbin Engineering University

基　　金：国家自然科学基金资助项目(61275010);教育部博士点基金资助项目(20132304110007);黑龙江省自然科学基金资助项目(F201409);中央高校基本科研业务费重大项目(HEUCFD1410)

摘　　要：针对传统的高光谱数据解混方法中存在的解混精度不高、丰度图模糊的缺陷,提出一种基于相关向量机的高光谱图像解混方法(unmixing algorithm based on relevance vector machine,UARVM)。其核心思想是采用改进的一对余型的相关向量机将多分类问题转化为多个二分类的问题,且求取出每个样本所对应的归属类别的概率值,即丰度值来完成图像的解混。理论研究和仿真结果表明:相对于传统解混方法,UARVM解混精度高,丰度分布图效果好。Aiming at the defects of the low unmixing accuracy and the abundance map fuzzy existing in the traditional hyperspectral image unmixing methods, a new hyperspectral imagery unmixing algorithm based on the relevance vector machine （UARVM） is proposed in this paper. The core idea of the proposed UARVM is to improve the one- against-rest relevance vector machine, which changes the multi-classification problem into the multiple binary-classification problem, and then to solve each sample＇s corresponding attribution class probability value, i.e. the abundance, to complete the unmixing process of the hyperspectral imagery. Theoretical analysis and simulation results show that, compared with the traditional unmixing methods, the UARVM method has better unmixing performance and abundance mad effect.

关 键 词：高光谱图像 UARVM 丰度 相关向量机 解混 

分 类 号：TN911.73[电子电信—通信与信息系统]