自动化学报2018，Vol.44Issue(1)：116-128,13.DOI:10.16383/j.aas.2018.c160414
一种基于协同稀疏和全变差的高光谱线性解混方法
A Novel Linear Hyperspectral Unmixing Method Based on Collaborative Sparsity and Total Variation
摘要
Abstract
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 the collaborative sparsity prior plays an important role in further accuracy improvement of HSI spatial-spectral unmixing. At last,the proposed model is solved by the alternating direction method of multipliers. Experimental results on simulated hyperspectral data quantitatively validate that the our method outperforms those state-of-the-art algorithms,and the experimental results on real hyperspectral data qualitatively verify the effectiveness of the algorithm.关键词
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Key words
Hyperspectral image(HIS)/collaborative sparsity/total variation(TV)/linear spectral unmixing/alternating direction method of multipliers引用本文复制引用
陈允杰,葛魏东,孙乐..一种基于协同稀疏和全变差的高光谱线性解混方法[J].自动化学报,2018,44(1):116-128,13.基金项目
国家自然科学基金(61672291,61601236,61471199,61571230),江苏省自然科学基金(BK20150923)资助Supported by National Natural Science Foundation of China(61672291,61601236,61471199,61571230)and Natural Science Foundation of Jiangsu Province(BK20150923) （61672291,61601236,61471199,61571230）
