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

Gu, Xiaowei and Angelov, Plamen Parvanov (2019) Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE. ISBN 9781728119861

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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 as the feature descriptor, 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.

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Contribution in Book/Report/Proceedings
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ID Code:
131861
Deposited By:
Deposited On:
12 Mar 2019 09:10
Refereed?:
Yes
Published?:
Published
Last Modified:
31 May 2020 00:11