Hyperspectral Images Unmixing Based on Nonnegative Matrix Factorization Optimized by Salp Swarm Algorithm

In order to solve the problem that robust nonnegative matrix factorization is sensitive to the initial values and usually trapped in a local optimum when applied to hyperspectral images processing,a new hyperspectral image unmixing algorithm based on robust nonnegative matrix factorization optimized by salp swarm algorithm is proposed.The algorithm is based on robust linear mixed model.Under the framework of robust nonnegative matrix factorization,the global search capability is enhanced by replacing the multiplicative iteration strategy with the salp swarm optimization algorithm,the global optimal solution satisfying the objective function is randomly searched in the constrained space.And then,the hyperspectral image unmixing is effectively fulfilled.The experimental results on synthetic data and real remote sensing data indicate that the algorithm proposed can effectively avoid the limitation of RNMF falling into the local optimal solution,which has better performance of unmixing.