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A Deep Rule-based Approach for Satellite Scene Image Analysis

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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A Deep Rule-based Approach for Satellite Scene Image Analysis. / Gu, Xiaowei; Angelov, Plamen Parvanov.
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. p. 2778-2783 (2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gu, X & Angelov, PP 2018, A Deep Rule-based Approach for Satellite Scene Image Analysis. in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp. 2778-2783, IEEE SMC 2018 conference, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00474

APA

Gu, X., & Angelov, P. P. (2018). A Deep Rule-based Approach for Satellite Scene Image Analysis. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2778-2783). (2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)). IEEE. https://doi.org/10.1109/SMC.2018.00474

Vancouver

Gu X, Angelov PP. A Deep Rule-based Approach for Satellite Scene Image Analysis. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE. 2018. p. 2778-2783. (2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)). doi: 10.1109/SMC.2018.00474

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov. / A Deep Rule-based Approach for Satellite Scene Image Analysis. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. pp. 2778-2783 (2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)).

Bibtex

@inproceedings{802104ec171246a9b57f3bad5c6f80a2,
title = "A Deep Rule-based Approach for Satellite Scene Image Analysis",
abstract = "Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF-THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; IEEE SMC 2018 conference ; Conference date: 07-10-2018",
year = "2018",
month = oct,
day = "7",
doi = "10.1109/SMC.2018.00474",
language = "English",
isbn = "9781538666517",
series = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
publisher = "IEEE",
pages = "2778--2783",
booktitle = "2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
url = "http://www.smc2018.org/",

}

RIS

TY - GEN

T1 - A Deep Rule-based Approach for Satellite Scene Image Analysis

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/10/7

Y1 - 2018/10/7

N2 - Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF-THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.

AB - Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF-THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.

U2 - 10.1109/SMC.2018.00474

DO - 10.1109/SMC.2018.00474

M3 - Conference contribution/Paper

SN - 9781538666517

T3 - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

SP - 2778

EP - 2783

BT - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

PB - IEEE

T2 - IEEE SMC 2018 conference

Y2 - 7 October 2018

ER -