Recently, many methods for hyperspectral unmixing have been proposed. These methods are often based on nonnegative matrix factorization (NMF), which naturally inherits the non-negative advantage and is in line with the common sense of physics. Although there are many ways to perform NMF-based hyperspectral unmixing, these methods can only unmix one hyperspectral image at a time. In practice, we may often collect two or more similar hyperspectral images, and the end of the hyperspectral images of the signal could be only slightly different. Traditional NMF-based hyperspectral unmixing methods cannot take advantage of the fact that different hyper-spectral images may have similar or even the same end-element signals. Accordingly, in order to improve the performance of NMF-based hyperspectral unmixing, we present an algorithm in this paper that can process two hyperspectral images, simultaneously, and makes full use of the available information when most of the signals at the two end-points are similar. This improves the effect of end-element extraction in hyperspectral unmixing evidenced by experimental results on both synthetic and real-world data.

中文翻译：

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用于城市计算的联合高光谱分解

最近，已经提出了许多用于高光谱分解的方法。这些方法通常基于非负矩阵分解（NMF），它自然地继承了非负优势，并且符合物理学的常识。尽管有很多方法可以执行基于NMF的高光谱分解，但是这些方法一次只能分解一个高光谱图像。在实践中，我们可能经常收集两个或更多个相似的高光谱图像，并且信号的高光谱图像的末端可能仅略有不同。传统的基于NMF的高光谱分解方法无法利用以下事实：不同的高光谱图像可能具有相似甚至相同的最终元素信号。因此，为了改善基于NMF的高光谱分解的性能，我们在本文中提出了一种算法，该算法可以同时处理两个高光谱图像，并且当两个端点处的大多数信号相似时，可以充分利用可用信息。通过合成和真实数据的实验结果证明，这改善了高光谱解混中末端元素提取的效果。