A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images

As the spectral dimension of hyperspectral images increases, band selection becomes more and more important when using hyperspectral data. Evolutionary algorithms have been applied to the band selection problem of hyperspectral images, but most of existing methods focus only on single indicator, ignoring the whole characteristics of hyperspectral image. In this paper we study a multi-objective artificial bee colony approach for the band selection problem of hyperspectral images. Firstly, a new multi-objective unsupervised band selection model is proposed by using both band correlation and information amount. Secondly, a multi-strategy integrated multi-objective artificial bee colony algorithm (MABC-BS) is proposed to deal with the band selection model above. Several new operators, including the multi-direction search strategy, the x-space crowing degree-based search strategy, and the adaptive mutation, are developed to enhance the proposed algorithm. Compared with eight representative algorithms on three typical test problems, experimental results on the classification performance of four classifiers (i.e., Random Forest, SVM, KNN, ObRaF) show that the proposed algorithm is a powerful approach for tackling the problem of hyperspectral band selection.

Automated summary

A multi-objective artificial bee colony approach is proposed for band selection problem in hyperspectral images, which uses a new multi-objective unsupervised band selection model and several new operators to enhance algorithm performance. Experimental results indicate that the proposed algorithm performs well in addressing hyperspectral band selection problem.