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

  • Xiaowei Gu
  • Plamen P. AngelovEmail author
Conference paper
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 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computing and CommunicationsLancaster UniversityLancasterUK
  2. 2.Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA)Lancaster UniversityLancasterUK
  3. 3.Technical UniversitySofiaBulgaria

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