Classification of Hyperspectral Remote Sensing Image Using Hierarchical Local-Receptive-Field-Based Extreme Learning Machine

Abstract

This letter proposes a novel classification approach for a hyperspectral image (HSI) using a hierarchical local-receptive-field (LRF)-based extreme learning machine (ELM). As a fast and accurate pattern classification algorithm, ELM has been applied in numerous fields, including the HSI classification. The LRF concept originates from research in neuroscience. Considering the local correlations of spectral features, it is promising to improve the performance of HSI classification by introducing the LRFs. Recent research on deep learning has shown that hierarchical architectures with more layers can potentially extract abstract representation and invariant features for better classification performance. Therefore, we further extend the LRF-based ELM method to a hierarchical model for HSI classification. Experimental results on two widely used real hyperspectral data sets confirm the effectiveness of the proposed HSI classification approach.