For hyperspectral unmixing, a multi-scale spatial regularization method based on a modified image segmentation algorithm to generate super-pixels is proposed in which the super-pixels are used to extract contextual information from spatial correlations and spectral similarity in hyperspectral images (HSIs). The unmixing problem is decomposed into two simple unmixing subproblems regarding the approximate super-pixels and the original pixels. The unmixing results of these two subproblems have spatial-correlation constraints. Introducing a novel regularization term to constrain the abundance matrix to promote the homogeneous abundances helps in making effective use of the spatial correlations and spectral similarity of the abundances from HSIs. Experimental results obtained from synthetic data demonstrate that the proposed algorithm yields an accuracy greater than other conventional methods.

对于高光谱解混，提出了一种基于改进的图像分割算法生成超像素的多尺度空间正则化方法，其中超像素用于从高光谱图像（HSI）中的空间相关性和光谱相似性中提取上下文信息。分解问题被分解为关于近似超像素和原始像素的两个简单分解子问题。这两个子问题的分解结果具有空间相关性约束。引入新的正则化项来约束丰度矩阵以促进均匀丰度，有助于有效利用 HSI 丰度的空间相关性和光谱相似性。