Abstract. Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band scaling factor to the endmember scaled spectrum. The total variation constraint is used to smooth the spatial distribution of both endmember and band scaling factors and then alternating iterative optimization is applied to solve the optimization problem. Experiments conducted with both simulated and real hyperspectral data sets indicate that the proposed algorithm is effective in hyperspectral unmixing and is superior to other state-of-the-art algorithms based on spectral variability.

中文翻译：

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具有光谱可变性的高光谱解混的端元和波段组合模型

摘要。光谱变异性是高光谱解混的最大限制因素之一，因此进一步研究光谱变异性特征对提高解混精度具有重要意义。在不同辐照条件下进行模拟后，通过向端元缩放光谱引入带缩放因子，提出了结合端元和带的线性混合模型。总变异约束用于平滑端元和带缩放因子的空间分布，然后应用交替迭代优化来解决优化问题。对模拟和真实高光谱数据集进行的实验表明，所提出的算法在高光谱分离方面是有效的，并且优于其他基于光谱可变性的最新算法。