期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 13, 期 3, 页码 434-438出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2016.2517178
关键词
Deep learning; extreme learning machine (ELM); hyperspectral image (HSI) classification; local receptive field (LRF)
类别
资金
- National Natural Science Foundation of China [61125201, 61303070, U1435219, 61402507, 61402499]
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.
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