Spectral and Spatial Kernel Extreme Learning Machine for Hyperspectral Image Classification

Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can't extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.