In this work

we proposed a hyperspectral unmixing method based on the spectrally weighted collaborative sparsity and the total variation

aiming at alleviating the lack of the sparsity of abundance in traditional methods and fully exploiting the spatial information. On the one hand

the spectral factors are utilized to estimate the weights in order to enforce the sparsity of nonzero rows

thus improving the collaborative sparsity among all the pixels. On the other hand

the total variation based spatial regularization is employed to reinforce the smoothness within the homogenous regions

hence improving the accuracy of unmixing. The model is solved by the well-known alternating direction method of multiplier

in which the spectral factor based weights and the abundance coefficients are iteratively optimized using both the internal and external loops. The experimental results obtained from the simulated and the real datasets indicate that the proposed method could significantly improve the performance of unmixing compared to the other state-of-the-art methods.