How to accurately characterize the sparse regularization function which is easy to be solved is difficult for sparse hyperspectral unmixing.Transformed L1TL1 regularization function is a one parameter family of bilinear transformations composed with the absolute value function,similar to Lp norm with p∈0,1,it represents any norms from L0 to L1 exactly through a nonnegative parameter a∈0,,and satisfies unbiasedness,sparsity and Lipschitz continuity properties.In this paper,we study TL1 regularization function and propose sparse hyperspectral unmixing model with TL1 regularization.Meanwhile,a difference of convex algorithm for TL1 in computing TL1 regularized sparse unmixing problems is presented.Experimental results on both simulated and real hyperspectral data demonstrate that the TL1 regularization function describes the sparsity of endmembers more accurately and the proposed algorithm is much more accurate than SUnSAL algorithm.