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.%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.
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