Ant colony optimization for supervised and unsupervised hyperspectral band selection

In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost. Both supervised (i.e., Jeffries-Matusita distance) and unsupervised (i.e., simplex volume) selection criteria are investigated. The experimental results demonstrate that the classification accuracy on the selected bands is higher than using all bands, and ACO-based methods can outperform the widely used sequential forward selection (SFS) method.

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