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A Multi-Stream Deep Rule-Based Ensemble System for Aerial Image Scene Classification

Gu, Xiaowei and Angelov, Plamen (2021) A Multi-Stream Deep Rule-Based Ensemble System for Aerial Image Scene Classification. In: Handbook on Computer Learning and Intelligence. World Scientific, Singapore. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90393)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

Aerial scene classification is the key task for automated aerial image understanding and information extraction, but is highly challenging due to the great complexity and real-world uncertainties exhibited by such images. To perform precise aerial scene classification, in this research, a multi-stream deep rule-based ensemble system is proposed. The proposed ensemble system consists of three deep rule-based systems that are trained simultaneously on the same data. The three ensemble components employ ResNet50, DenseNet121 and InceptionV3 as their respective feature descriptors because of the state-of-the-art performances the three networks have demonstrated on aerial scene classification. The three networks are fine tuned on aerial images to further enhance their discriminative and descriptive abilities. Thanks to its prototype-based nature, the proposed approach is able to self-organize a transparent ensemble predictive model with prototypes learned from training images and perform highly explainable joint decision-making on testing images with greater precision. Numerical examples on both benchmark aerial image sets and satellite sensor images demonstrated the efficacy of the proposed approach, showing its great potential in solving real-world problems.

Item Type: Book section
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 08:46 UTC
Last Modified: 28 Sep 2021 08:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90393 (The current URI for this page, for reference purposes)
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