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Remote sensing image classification using extreme learning machine-guided collaborative coding

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Abstract

Remote sensing image classification is a very challenging problem and covariance descriptor can be introduced in the feature extraction and representation process for remote sensing image. However, due to the reason that covariance descriptor lies in non-Euclidean manifold, conventional extreme learning machine (ELM) cannot effectively deal with this problem. In this paper, we propose an improved ELM framework which incorporates the collaborative coding to tackle the covariance descriptor classification problem. First, a new ELM-guided dictionary learning and coding model is proposed. Then the iterative optimization algorithm is developed to solve the model. By evaluating the proposed approach on the UCMERCED high-resolution aerial image dataset, we show the effectiveness of the proposed strategy.

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Authors and Affiliations

  1. High-Tech Institute of Xian, Xian, 710025, Shaanxi, People’s Republic of China

    Chunwei Yang, Shicheng Wang & Shouyi Liao

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, People’s Republic of China

    Chunwei Yang & Huaping Liu

Authors
  1. Chunwei Yang
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  2. Huaping Liu
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  3. Shicheng Wang
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  4. Shouyi Liao
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Corresponding author

Correspondence to Huaping Liu.

Additional information

This work was supported in part by the National Key Project for Basic Research, China, under Grant 2013CB329403, in part by the National Natural Science Foundation of China under Grant 61327809, and in part by the National High-Tech Research and Development Plan under Grant 2015AA042306.

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Yang, C., Liu, H., Wang, S. et al. Remote sensing image classification using extreme learning machine-guided collaborative coding. Multidim Syst Sign Process 28, 835–850 (2017). https://doi.org/10.1007/s11045-016-0403-6

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  • DOI: https://doi.org/10.1007/s11045-016-0403-6

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