LAUSR

In order to unmix the hyperspectral imagery (HSI) with better performance, this letter proposes a correntropy-based autoencoder-like nonnegative matrix factorization (NMF) (CANMF) with total variation (CANMF-TV) method. NMF is extensively applied to unmix the mixed pixels. However, it only reconstructs the original data from the abundances in endmember space. To directly project the original data space into the endmember space, and then achieve the abundance matrix, we first exploit an autoencoder-like NMF for hyperspectral unmixing, which integrates both decoder and encoder. Considering that HSI is typically degraded by noise, the correntropy-induced metric (CIM) is introduced to construct a CANMF model. In addition, TV regularizer is imposed into the CANMF model so as to preserve the spatial-contextual information by promoting the piecewise smoothness of abundances. Finally, a series of experiments are conducted on both synthetic and real data sets, demonstrating the effectiveness of the proposed CANMF-TV method over comparison.