As a representative structural property, the sparsity of ground covers’ distribution in hyperspectral images (HSIs) has been extensively applied to improve spectral unmixing in years. It is worth leveraging the close relationship between the sparsity of abundances and the spatial information of HSIs to obtain more reasonable sparse unmixing results. In this letter, a novel multilevel reweighted sparse unmixing method using superpixel segmentation and particle swarm optimization (MRSUPSO) is proposed. Three sparse reweighted factors are finely designed at different local and global spatial levels. The first two local reweighted factors are constructed according to the sparseness and the low-rank property of pixels’ abundances in the generated superpixels. The third global reweighted factor is given by considering the change of the sparseness of each material abundance map in the entire HSI. Then, a new sparse constraint is imposed, which can effectively facilitate the correct expression of abundances’ sparsity during unmixing. Moreover, PSO based on double swarms with dimension division is employed to solve the unmixing problem and enhance the unmixing robustness. Experimental results of both simulated and real hyperspectral data validate that the proposed method can produce accurate unsupervised sparse unmixing results.

中文翻译：

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使用超像素分割和粒子群优化的多级重新加权稀疏高光谱分解


作为一种代表性的结构特性，高光谱图像（HSI）中地被植物分布的稀疏性多年来已被广泛应用于改善光谱分解。值得利用丰度稀疏性与HSI空间信息之间的密切关系来获得更合理的稀疏解混结果。在这封信中，提出了一种使用超像素分割和粒子群优化（MRSUPSO）的新颖的多级重新加权稀疏分解方法。在不同的局部和全局空间层面上精心设计了三个稀疏重加权因子。前两个局部重加权因子是根据生成的超像素中像素丰度的稀疏性和低秩特性构造的。第三个全局重加权因子是考虑整个HSI中每个物质丰度图的稀疏度的变化而给出的。然后，施加新的稀疏约束，可以有效促进解混合过程中丰度稀疏性的正确表达。此外，采用基于双群维数划分的粒子群算法来解决解混问题，增强解混的鲁棒性。模拟和真实高光谱数据的实验结果验证了所提出的方法可以产生准确的无监督稀疏解混合结果。