线性高光谱解混模型综述
投稿时间:2018-01-05  修订日期:2018-03-30  点此下载全文
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作者单位E-mail
袁静 清华大学 yuanjing20110824@sina.com.cn 
章毓晋 清华大学  
基金项目:国家自然科学基金项目 (NNSF:61673234和NNSF:U1636124)
中文摘要:高光谱遥感技术具有强大的地物探测能力,其谱分率高以及空间分辨率低导致的大量的混合像元阻碍了高光谱技术的发展.对于米级以下的高光谱图像,线性混合模型由于其物理上的可释性以及数学上的可操作性能够很好地为混合像元建模.其作为光谱解混的基础,亦受到了广泛的关注.线性光谱技术发展初期,出现了很多易处理和较准确解混的数学模型.然而,由于观测噪声、环境条件、端元变异性和数据集大小等问题使得线性解混依然是一个具有挑战性的不适定的逆问题.本文通过整理近五年的文献资料,分别从矩阵分解、原型分析、贝叶斯方法以及稀疏解混四个方面介绍线性解混的数学模型的发展现状以及面临的问题.
中文关键词:高光谱解混  矩阵分解  贝叶斯方法  原型分析;稀疏解混
 
An Overview on Linear Hyperspectral Unmixing
Abstract:Hyperspectral remote sensing technology contribute significantly to earth observation. In hyperspectral images (HSIs), the spectral vector of each pixel contains hundreds or even thousands of elements, which provides rich spectral information to efficiently identify and distinguish different types of land cover. However, due to the limited spatial resolution and the complexity of surface features, mixed pixels are common in HSIs. The existence of numerous mixed pixels conflicts with the demands for accurate recognition and interpretation of the material properties of the pixels. Hyperspectral unmixing (HU), which decomposes the mixed pixels into a set of constituent materials called “endmembers”, as well as the corresponding mixture coefficients referred to as “abundances”, was developed to alleviate the mixed pixels problem. Linear unmixing as the basis of HU, due to its physical explanatory and mathematic maneuverability, has been widely concerned. At the beginning, there are many mathematical models to handle Linear Hyperspectral Unmixing(LHU). However, the observation noise, the environmental conditions, the endmember variability and dataset size provide lots of challenges to the inverse problem of ill-posedness. The paper summarizes the literature of the past five years from four aspects: matrix decomposition, archetype analysis, bayesian method and sparse regression to unveil the state-of-art models and problems of linear unmixing.
keywords:hyperspectral unmixing, matrix factorization, bayesian method, archetype analysis, sparse regression
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