稀疏正则化函数的选取直接影响到稀疏非负矩阵分解高光谱解混的效果。目前, 主要采用L0或L1范数作为稀疏度量。L0稀疏性好, 但求解困难; L1求解方便, 但稀疏性差。提出一种近似稀疏模型, 并将其引入到多层非负矩阵分解(AL0-MLNMF)的高光谱解混中, 将观测矩阵进行多层次稀疏分解, 提高非负矩阵分解高光谱解混的精度, 提升算法的收敛性。仿真数据和真实数据实验表明: 该算法能够避免陷入局部极值, 提高非负矩阵分解高光谱解混性能, 算法精度上比其他几种算法都有较大的提升效果, RMSE降低0.001~1.676 7, SAD降低0.002~0.244 3。

The selection of sparse regularization functions directly affects the effect of sparse non-negative matrix factorization of hyperspectral unmixing. At present, the L0 or L1 norms are mainly used as sparse measures. L0 has good sparsity, but it is difficult to solve; L1 is easy to solve, but the sparsity is poor. An approximate sparse model was presented, and was applied to the multi-layer NMF (AL0-MLNMF) in hyperspectral unmixing. The algorithm made the observation matrix multilevel sparse decomposition improve the precision of hyperspectral unmixing, and improve the convergence of the algorithm. The simulation data and real data show that the algorithm can avoid falling into the local extremum and improve the NMF hyperspectral unmixing performance. Algorithm accuracy has greater improvement effect than several other algorithm, RMSE reduces 0.001-1.676 7 and SAD reduces 0.002-0.244 3.