Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images

Faxian Cao, Zhijing Yang, Jinchang Ren, Wenchao Chen, Guojun Han, Yuzhen Shen

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Abstract

Although Extreme Learning Machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) Ineffective feature extraction in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective feature extraction from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers (ADMM). This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSLM). The loopy belief propagation (LBP) is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches.
Original languageEnglish
Number of pages14
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusAccepted/In press - 14 Feb 2019

Keywords

  • extreme learning machine
  • hyperspectral images
  • local block multilayer sparse ELM
  • loopy belief propagation
  • alternative direction method of multipliers

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    Faxian Cao

    Jinchang Ren (Host)
    Nov 2018May 2019

    Activity: Hosting a visitor typesHosting an external, non-academic visitor

    Zhijing Yang

    Jinchang Ren (Host)
    May 2017Jul 2018

    Activity: Hosting a visitor typesHosting an academic visitor

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