Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

3 Citations (SciVal)

Abstract

In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks for feature extraction, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF...THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.
Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE Press
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
DOIs
Publication statusE-pub ahead of print - 30 Sept 2019
Externally publishedYes

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Keywords

  • aerial scene classification
  • deep convolutional neural network
  • deep rule-based
  • ensemble feature descriptor

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