A novel nonlinear hyperspectral image unmixing algorithm based on differential search is proposed for solving the limitations of linear mixing model.The reconstruction error is used as the objective function for unmixing based on generalized bilinear model and the nonlinear unmixing is transformed into the optimization problem.The parameters in objective function are mapped onto the location variables of the search process and the differential search algorithm is used to optimize the objective function.In the optimization process

the constraint conditions for hyperspectral image unmixing are fulfilled by implementing the search range controlling strategy.And then

the abundance and the nonlinear parameters for unmixing can be obtained.Experiments on synthetic data and real data validate that the proposed nonlinear unmixing algorithm can effectively overcome the limitations of linear unmixing algorithm

as well as the local convergence of gradient optimization method

and the performance of the proposed algorithm is better than other state-of-the-art hyperspectral image unmixing algorithms.