Firefly-Algorithm-Inspired Framework With Band Selection and Extreme Learning Machine for Hyperspectral Image Classification

A firefly algorithm (FA) inspired band selection and optimized extreme learning machine (ELM) for hyperspectral image classification is proposed. In this framework, FA is to select a subset of original bands to reduce the complexity of the ELM network. It is also adapted to optimize the parameters in ELM (i.e., regularization coefficient C, Gaussian kernel σ, and hidden number of neurons L). Due to very low complexity of ELM, its classification accuracy can be used as the objective function of FA during band selection and parameter optimization. In the experiments, two hyperspectral image datasets acquired by HYDICE and HYMAP are used, and the experiment results indicate that the proposed method can offer better performance, compared with particle swarm optimization and other related band selection algorithms.