TY - CHAP

T1 - A multistream deep rule-based ensemble system for aerial image scene classification

AU - Gu, Xiaowei

AU - Angelov, Plamen P.

N1 - Publisher Copyright: © 2022 World Scientific Publishing Company.

PY - 2022/6/29

Y1 - 2022/6/29

N2 - 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 multistream 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 based 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.

AB - 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 multistream 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 based 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.

UR - http://www.scopus.com/inward/record.url?scp=85142905231&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:85142905231

SN - 9789811245145

VL - 2

SP - 661

EP - 695

BT - Deep Learning, Intelligent Control and Evolutionary Computation

A2 - Angelov, Plamen

PB - World Scientific Publishing Co.

ER -