Most image fusion algorithms based on hyperspectral unmixing perform worse with the lower spatial resolution of hyperspectral image (HSI) for the reason that the estimated endmembers and abundance deviate from the truth value. Therefore, it is more meaningful to unmix the low spatial resolution hyperspectral image (LRHSI) accurately, which is also helpful to improve the image fusion performance. In order to enhance the spatial resolution of LRHSI, this article proposes an alternating direction iterative nonnegative matrix factorization (ADINMF) based on linear hyperspectral unmixing algorithm. It takes multispectral image as a constraint to improve the spatial resolution of LRHSI. First, we use blind source separation to initialize the endmember and abundance of hyperspectral and multispectral images, respectively. Then, we alternately update the endmembers and abundance in the framework of nonnegative matrix factorization by multiplication iterative algorithm. The updated endmembers and abundance are constrained to each other. We compare the experimental results of simulated dataset and three groups of real datasets. Experimental results show that the proposed method not only accurately extracts the endmembers of LRHSI, but also obtains a significant fusion performance improvement.

中文翻译：

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多光谱和高光谱数据融合的交替方向迭代非负矩阵分解解混

大多数基于高光谱解混的图像融合算法在高光谱图像（HSI）的空间分辨率较低的情况下表现更差，原因是估计的端元和丰度偏离了真值。因此，对低空间分辨率高光谱图像（LRHSI）进行准确解混更有意义，这也有助于提高图像融合性能。为了提高LRHSI的空间分辨率，本文提出了一种基于线性高光谱解混算法的交替方向迭代非负矩阵分解（ADINMF）。以多光谱图像为约束，提高LRHSI的空间分辨率。首先，我们使用盲源分离来分别初始化高光谱和多光谱图像的端元和丰度。然后，我们通过乘法迭代算法在非负矩阵分解框架中交替更新端元和丰度。更新的端元和丰度相互限制。我们比较了模拟数据集和三组真实数据集的实验结果。实验结果表明，所提出的方法不仅准确地提取了LRHSI的端元，而且融合性能得到了显着的提升。