Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images.

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Jiang, Mengying; Ling, Wing-Kuen

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.

Citation

CAO, F., YANG, Z., REN, J., JIANG, M. and LING, W.-K. 2017. Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors [online], 17(11), article number 2603. Available from: https://doi.org/10.3390/s17112603

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2017
• Online Publication Date: Nov 13, 2017
• Publication Date: Nov 30, 2017
• Deposit Date: Jul 23, 2024
• Publicly Available Date: Jul 23, 2024
• Journal: Sensors
• Print ISSN: 1424-8220
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 11
• Article Number: 2603
• DOI: https://doi.org/10.3390/s17112603
• Keywords: Hyperspectral images (HSI); Extreme learning machine (ELM); Spectral-spatial classification; Discriminative random fields (DRF); Loopy belief propagation (LBP)
• Public URL: https://rgu-repository.worktribe.com/output/2059168