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Recently, sparse unmixing focuses on finding an optimal subset of spectral signatures in a large spectral spetral library. In most previous work concerned with the sparse unmixing, the linear mixture model has been widely used to determine and quantify the abundance of materials in mixed piexels(1). In this paper, we propose a new sparse unmxing method based on an approximate sparsity regularization model(2). The approximate sparsity regularizer is much easier to solve than the L0 regularizer and has stronger sparsity than the L1 regularizer. What's more, a variable splitting and augmented Lagrangian methods introduced in to solve the proposed problem. Our numerical results on sparse unmixing illustration the efficiency of approximate sparsity a under the SUnSAL algorithm framework, compared to the L1 norm.

Sparse Unmixing using an approximate L0 Regularization