Hyperspectral Image Reconstruction Based on Reference Point Nondominated Sorting Genetic Algorithm

Abstract

Spatial and spectral features of hyperspectral imagery reconstruction have gained increasing attention in the latest years. Based on the study of orthogonal matching pursuit (OMP) idea, a hyperspectral image reconstruction algorithm based on reference point nondominated sorting genetic algorithm (NSGA) is proposed. Instead of directly reconstructing the entire hyperspectral data as a traditional OMP reconstruction algorithm, the proposed algorithm explores the idea of the evolution process in the reconstruction. The Gabor redundancy dictionary is established as the sparse basis of hyperspectral images, and the reconstruction model of multiobjective optimization is constructed. In the reconstruction process, the NSGA-III algorithm is used to find the optimal atoms to represent the original signal, and Hermitian inversion lemma is also used to realize the recursive update of the residuals. The initial solution generation, the definition of reference points, the association and niche-preservation operation, and the crossover and mutation operation in NSGA-III are presented in detail. Experimental results on hyperspectral data demonstrate that the proposed algorithm could maintain the reconstruction accuracy, as well as the computational efficiency, and are superior to the state-of-the-art reconstruction algorithms. The proposed algorithm could be applied in the classification and unmixing in hyperspectral images.