Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

This work presents a new urban land cover classification framework using the firefly algorithm （FA） optimized extreme learning machine （ELM）. FA is adopted to optimize the regularization coefficient C and Ganssian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.