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INNSBDDL 2019: Recent Advances in Big Data and Deep Learning pp 257–266Cite as

A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification

Part of the Proceedings of the International Neural Networks Society book series (INNS,volume 1)

Abstract

This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.

Keywords

  • Deep rule-based
  • Remote sensing scene classification
  • Semi-supervised learning

P. P. Angelov—Honorary Professor

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  • DOI: 10.1007/978-3-030-16841-4_27
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Author information

Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, UK

    Xiaowei Gu & Plamen P. Angelov

  2. Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA), Lancaster University, Lancaster, UK

    Xiaowei Gu & Plamen P. Angelov

  3. Technical University, 1000, Sofia, Bulgaria

    Plamen P. Angelov

Authors
  1. Xiaowei Gu
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  2. Plamen P. Angelov
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Corresponding author

Correspondence to Plamen P. Angelov .

Editor information

Editors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Luca Oneto

  2. Department of Mathematics, University of Padova, Padua, Italy

    Nicolò Navarin

  3. Department of Mathematics, University of Padova, Padua, Italy

    Alessandro Sperduti

  4. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Davide Anguita

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Gu, X., Angelov, P.P. (2020). A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_27

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