ELM-based spectral-spatial classification of hyperspectral images using bilateral filtering information on spectral band-subsets

As single-layer feed-forward neural networks, extreme learning machine (ELM) has recently been used with success for the classification of hyperspectral images (HSIs). However, the results of pure pixel-wise spectral classifiers often appear very noisy with limited training samples. To further improve the accuracy, we propose a novel spectral-spatial information integrating scheme for pixel-wise kernel ELM-based classifier. In particular, we show that a spatial bilateral filtering information on spectral band-subsets can significantly improve the accuracy of the pixel-wise kernel ELM based classifier. The benefits of the proposed method are twofold: 1) spectral structural similarity guided band-subsets partition and 2) incorporating the spectral-spatial information by bilateral filtering. Experiments on the widely used real HSI demonstrate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy and low computational cost.