An adaptive evolutionary-reinforcement learning algorithm for hyperspectral band selection

Band selection is one of the most important considerations for hyperspectral imaging (HSI) datasets that involves selecting fewer bands with higher classification accuracy. As a combinatorial optimization problem, evolutionary algorithm has been applied in the field, but the optimization ability and number of selected bands are limited with the control of exploration and exploitation. In this paper, an adaptive evolutionary-reinforcement learning algorithm (ERLA) is proposed for band selection of HSI datasets, only a suitable phase contributes to updating the corresponding agents in each iteration, and the coding length is adaptively lessened with the potential solution space. In addition, reinforcement learning is used to choose a feasible action in each iteration, and the unselected bands are removed from the candidate band combination in the previous state. A simulation study is conducted on four public HSI datasets, where the proposed framework has delivered an overall accuracy of 98% for 12% of bands in Chikusei dataset, 92% for 11% in KSC dataset, 93% against 19% for Xiongan dataset, and 97% in Longkou dataset with 10% bands retained, demonstrating that the proposed framework outperforms other approaches with several targets, and that the number of selected bands is obviously decreased with satisfactory classification accuracy.