Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization

Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the $L_{1}$ regularizer. Unfortunately, the $L_{1}$ regularizer cannot enforce further sparsity when the full additivity constraint of material abundances is used, hence limiting the practical efficacy of NMF methods in hyperspectral unmixing. In this paper, we extend the NMF method by incorporating the $L_{1/2}$ sparsity constraint, which we name $L_{1/2}$…

• AG
  Australian Government
• NN
  National Natural Science Foundation of China
• NS
  Natural Science Foundation of Guangdong Province
• AR
  Australian Research Council