Sparse unmixing using the improved whale optimized subspace matching pursuit algorithm
Zhicheng JIA, Xiao Zheng, Yanju GUO, Lei Chen · JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING · 2020
This paper proposes a whale optimized subspace matching pursuit algorithm to improve the precision of sparse unmixing of hyperspectral data. The proposed algorithm modifies the traditional whale group optimization algorithm by introducing the nonlinear population control parameters and evolution strategy and thus improves the convergence speed and convergence precision of the whale cluster optimization algorithm. Based on the improved subspace matching pursuit algorithm, the new algorithm uses the whale optimization algorithm to solve the abundance coefficients of the known end members, where the objective function is modeled as a constrained sparse regression. In order to improve the precision of the subspace matching pursuit algorithm, the minimum reconstruction error is taken as the criterion and the redundant end members with smaller coefficients are removed to the greatest extent. The simulation and real data experiments show that the proposed algorithm can effectively remove the redundant endmembers and has a higher precision of unmixing.