Improvements in sparse non-negative matrix factorization for hyperspectral unmixing algorithms

The hyperspectral unmixing technique is of great significance for hyperspectral data analysis. The non-negative matrix factorization (NMF)-based method is one of the most popular unmixing methods. However, the sparse constraints commonly used in NMF methods are not differentiable at zero, which affects the stability and accuracy of the results. In addition, sparse constraints are supposed to have endmember departure limitations. An improved sparse NMF algorithm is proposed. First, a biased L-1/2 sparse constraint is used to effectively explore the sparsity of abundances, which can also have a sparse effect under the condition of the abundance sum-to-one constraint. Second, to alleviate the endmember departure effect, a minimum energy difference constraint is introduced. Simulated and real-data experiments verify the advantages of the proposed method against state-of-the-art sparse NMF methods. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)