一种基于协同稀疏和全变差的高光谱线性解混方法

稀疏分解是高光谱图像（Hyperspectral image,HSI）解混中的常用方法,为了克服传统稀疏解混方法只重视挖掘空间相关性而忽视稀疏性精确刻画的缺点,本文提出一种新的基于协同稀疏和全变差（Total variation,TV）相结合的高光谱空谱联合线性解混方法,从而进一步提高解混的精度.该方法基于已知光谱库的高光谱稀疏线性回归模型,利用TV正则项对高光谱邻域像元间的相关性进行约束;同时,协同稀疏性被用来刻画丰度系数的行稀疏性,从而表明协同稀疏先验对空谱联合解混精度的提高至关重要;最后采用交替方向乘子法求解模型.模拟高光谱数据实验结果定量地验证本文方法能够比现有同类方法获得更精确的解混...

Full description

Saved in:
Bibliographic Details
Published in自动化学报 Vol. 44; no. 1; pp. 116 - 128
Main Author 陈允杰;葛魏东;孙乐
Format Journal Article
LanguageChinese
Published 南京信息工程大学数学与统计学院 南京210044%南京信息工程大学计算机与软件学院 南京210044 2018
Subjects
高光谱图像;协同稀疏;TV正则项;线性光谱解混;交替方向乘子法
线性光谱解混
高光谱图像
alternating direction method of multipliers
协同稀疏
TV正则项
linear spectral unmixing
交替方向乘子法
collaborative sparsity
Hyperspectral image(HIS)
total variation(TV)
Online AccessGet full text
ISSN0254-4156
1874-1029
DOI10.16383/j.aas.2018.c160414

Cover

More Information
Summary:稀疏分解是高光谱图像（Hyperspectral image,HSI）解混中的常用方法,为了克服传统稀疏解混方法只重视挖掘空间相关性而忽视稀疏性精确刻画的缺点,本文提出一种新的基于协同稀疏和全变差（Total variation,TV）相结合的高光谱空谱联合线性解混方法,从而进一步提高解混的精度.该方法基于已知光谱库的高光谱稀疏线性回归模型,利用TV正则项对高光谱邻域像元间的相关性进行约束;同时,协同稀疏性被用来刻画丰度系数的行稀疏性,从而表明协同稀疏先验对空谱联合解混精度的提高至关重要;最后采用交替方向乘子法求解模型.模拟高光谱数据实验结果定量地验证本文方法能够比现有同类方法获得更精确的解混结果,同时真实高光谱数据实验结果定性地验证了本文方法的有效性.
Bibliography:CHEN Yun-Jie1, GE Wei-Dong1, SUN Le2
Hyperspectral image （HSI）, collaborative sparsity, total variation （TV）, linear spectral unmixing, alternating direction method of multipliers
Sparse decomposition is one of the popular tools for hyperspectral unmixing. In order to overcome the short- comings of traditional sparse unmixing methods which only pay attention to the spatial correlation and neglect depicting sparsity accurately, we propose a new spatial-spectrally linear hyperspectral unmixing method based on collaborative sparsity and total variation （TV） regularization to further improve the accuracy of unmixing. This method is based on hyperspectral sparse linear regression model with a spectral library given in advance, in which the total variation is utilized to impose a constraint on the correlation between neighboring pixels of hyperspectral image （HSI）. Meanwhile, the collaborative sparsity is explored to depict the row-sparse characteristic of the fractional abundances, thus pointing out the fact that th
ISSN:0254-4156
1874-1029
DOI:10.16383/j.aas.2018.c160414