Hyperspectral image classification via kernel extreme learning machine using local receptive fields

This paper proposes a classification approach for hyperspectral image (HSI) using the local receptive fields based kernel extreme learning machine. Extreme learning machine (ELM) has drawn increasing attention in the pattern recognition filed due to its simpleness, speediness and good generalization ability. A kernel method is often used to promote ELM's performance, which is known as kernel ELM. The local receptive field concept originates from research in neuroscience. Considering the local correlations of spectral features, it is promising to improve the performance of HSI classification by combining local receptive fields with kernel ELM. Experimental results on the Pavia University dataset confirm the effectiveness of the proposed HSI classification method.