Supervised classification of hyperspectral images using local-receptive-fields-based kernel extreme learning machine

In this paper, we propose a local receptive fields (LRF) based kernel extreme learning machine (KELM) method for hyperspectral image (HSI) classification. As a single-hidden-layer feed-forward neural networks, kernel ELM has been used with success for classification of HSI. Considering local correlations in the spatial domain of HSI, the local random convolution nodes are introduced as LRF in the input layer. By using the LRF-based convolutional ELM feature learning on the principle component images, the proposed method can automatically learn rich feature hierarchies. All the learnt rich feature hierarchies are compressed to compact features, and the kernel ELM is applied to obtain the final classification results. Experimental results on the widely used real HSI data set indicate that the proposed approach outperforms several well-known classification methods.