Hyperspectral unmixing addressing spectral variability remains an important challenge. In this field, unmixing methods do not exploit the possible availability of some spectral information that corresponds to known spectra of some pure materials present in an acquired scene. In this work, a hyperspectral unmixing method, which considers not only the spectral variability phenomenon but also exploits one or more available known pure material spectra, is proposed. Such a combination, initially proposed here, constitutes the originality of the conducted work that distinguishes it from other investigations in the hyperspectral unmixing topic. The proposed method, based on an informed nonnegative matrix factorization technique, employs a partial structured additively-tuned linear mixing model that deals with spectral variability. Experimental results, based on real data, show that the designed informed algorithm, which addresses spectral variability, yields very satisfactory results and outperforms tested literature approaches. Thus, such an unmixing algorithm may be used for automatically detecting and mapping, using hyperspectral data, materials of interest whose spectra are known while dealing with their spectral variability.

中文翻译：

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用于解决光谱可变性的结构化加性调谐线性混合模型的高光谱分解与基于部分非负矩阵分解的方法

解决光谱变异性的高光谱分解仍然是一个重要的挑战。在该领域中，分解方法不利用与获取场景中存在的某些纯材料的已知光谱相对应的某些光谱信息的可能可用性。在这项工作中，提出了一种高光谱分解方法，该方法不仅考虑了光谱变异现象，还利用了一个或多个可用的已知纯材料光谱。这种最初在这里提出的组合构成了所进行工作的独创性，将其与高光谱分解主题的其他研究区分开来。所提出的方法基于知情的非负矩阵分解技术，采用部分结构化的加性调谐线性混合模型来处理光谱可变性。实验结果，基于真实数据，表明设计的解决光谱可变性的知情算法产生了非常令人满意的结果，并且优于经过测试的文献方法。因此，这种分解算法可用于使用高光谱数据自动检测和映射其光谱已知的感兴趣材料，同时处理它们的光谱可变性。