A Tri-evolutionary mechanism with information interaction of differential evolution for hyperspectral band selection

Band selection is one of the most important tasks for hyperspectral imaging (HSI) dataset, and fewer bands are extracted with satisfactory classification accuracy. As a combinatorial optimization problem, evolutionary algorithm (EA) is widely used in the field, each band is represented by a dimension. However, reducing the number of selected bands for a vast number of band combinations presents challenges. In this paper, a Tri-evolutionary mechanism with information interaction of differential evolution (TEDE) is proposed for hyperspectral band selection, all bands are divided into two parts based on their correlation with each band and label, two populations are independently trained on these parts, and the selected bands are combined to reconstruct the band combination. Subsequently, a new population is generated to obtain the optimal band subsets satisfying the criteria of information interaction. Experimental comparisons with EA-based, Co-evolution-based and newly proposed band selection methods are conducted to evaluate the performance of Tri-evolution, which demonstrate that TEDE outperforms other approaches in multiple metrics, notably reducing the number of selected bands while improving classification accuracy.