基于修正MCMC的端元可变的混合像元分解算法  

Variable Endmember Unmixing Algorithm Based on Correction MCMC Method

作　　者：胡霞[1] 宋现锋[1] 牛海山[1] 

机构地区：[1]中国科学院大学,北京100049

出　　处：《计算机科学》2013年第11期308-311,共4页Computer Science

基　　金：国家自然科学基金项目(40871032);国家973项目(2013CB733402)资助

摘　　要：传统的混合像元分解一般是基于固定端元的,然而实际上影像中像元并非都由完全相同的端元组成。基于波谱库,将端元选取和丰度反演合为一个步骤,抽象成一个估计参数的随机过程,在端元数目可变的前提下,基于可逆的跳跃式MCMC方法估计参数,从波谱库中选取端元并对混合像元进行线性解混。在状态转移过程中,加入端元的累积知识,以提高算法效率。这种算法不需要人工干预,能够实现自动化像元分解,并且具有较高的精度。实验表明,基于修正MCMC的端元可变的自动化解混算法在分解精度和稳定性方面均优于基于固定端元的混合像元分解方法。Traditional unmixing methods are based on the fixed endmember,and need to assume that the remonte sense image has pure pixel. In fact, this assumption is not necessarily true, and all pixels are not composed of the same end- members. This paper merged the endmember extration and unmixing into one step, and abstracted it to a random process based on a standard spectral library. Within the premise of variable number of endmembers, Reversible jump MCMC method was used to estimate parameters. The accumulated knowledge of endmembers was used during the state transition process,to improve algorithm efficiency. This algorithm does not require human intervention. It can achieve automated unmixing, and has a high accuracy. The experiments show that the algorithm based on MCMC is superior to the traditional unmixing method in both accuracy and stability.

关 键 词：像元分解 波谱库 端元可变 随机变量 MCMC 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]