Spectral unmixing (SU) has been a subject of particular attention in the hyperspectral imaging literature. Most SU algorithms are based on the linear mixing model (LMM), which assumes that one pixel of the image is the linear combination of a given number of pure spectra called endmembers, weighted by their coefficients called abundances. SU is a technique to identify these endmembers and their relative abundances. We present an LMM approach based on nonnegative matrix factorization, combining the minimum volume constraint (MVC) and Kullback–Leibler (KL) divergence referred to as KL-MVC. The proposed method is evaluated using synthetic images with different noise levels and real images with different methods of initialization, and high performance has been achieved compared with the widely used LMM-based methods.