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Sensors 2017, 17(11), 2603; https://doi.org/10.3390/s17112603

Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images

1
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 5 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
(This article belongs to the Section Remote Sensors)
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Abstract

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method. View Full-Text
Keywords: hyperspectral image (HSI); extreme learning machine (ELM); spectral-spatial classification; discriminative random field (DRF); loopy belief propagation (LBP) hyperspectral image (HSI); extreme learning machine (ELM); spectral-spatial classification; discriminative random field (DRF); loopy belief propagation (LBP)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cao, F.; Yang, Z.; Ren, J.; Jiang, M.; Ling, W.-K. Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images. Sensors 2017, 17, 2603.

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