Abstract. Hyperspectral unmixing is one of the most important procedures for remote sensing image processing. The multilayer non-negative matrix factorization (MLNMF)-based method has been widely used for hyperspectral unmixing due to its good performance for highly mixed data with multiple-decomposition structure. However, few works consider the spatial information in the image, which may enhance the performance. In order to solve this issue, we propose a homogeneous region regularized multilayer non-negative matrix factorization (HR-MLNMF) method for hyperspectral unmixing. In HR-MLNMF, the spatial information, depicted by the homogeneous region, is applied to regularize MLNMF, which could enhance the smoothness of each homogeneous spatial field to achieve better performance. Experiments on both synthetic and real datasets have validated the effectiveness of our method and shown that it has outperformed several state-of-the-art approaches of hyperspectral unmixing.

中文翻译：

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用于高光谱解混的均匀区域正则化多层非负矩阵分解

摘要。高光谱解混是遥感图像处理中最重要的过程之一。基于多层非负矩阵分解（MLNMF）的方法因其对具有多重分解结构的高度混合数据的良好性能而被广泛用于高光谱解混。然而，很少有作品考虑图像中的空间信息，这可能会提高性能。为了解决这个问题，我们提出了一种用于高光谱解混的均匀区域正则化多层非负矩阵分解（HR-MLNMF）方法。在 HR-MLNMF 中，由均匀区域所描绘的空间信息被应用于正则化 MLNMF，这可以增强每个均匀空间场的平滑度以获得更好的性能。