Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters

Hyperspectral unmixing is a process by which pixel spectra in a scene are decomposed into constituent materials and their corresponding fractions. Nonnegative matrix factorization (NMF) is a method recently developed to deal with matrix factorization. This paper proposes a hyperspectral unmixing algorithm using auto-NMF based on the L-curve theory. It is an approach to automatically estimate regularization parameters, which are manually chosen subjectively and difficultly in the traditional regularized non-negative matrix factorization (RNMF). We experiment traditional algorithms and auto-NMF on the synthetic data, better results are obtained from auto-NMF, indicating it is an effective technique for hyperspectral unmixing.