Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets

Yuanzhen Shuai; Ning Xin; Md Maruf Hasan; Bintao Hu; Tianhong Dai; Hengyan Liu

doi:10.1109/CyberC58899.2023.00015

Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets

Yuanzhen Shuai, Ning Xin, Md Maruf Hasan, Bintao Hu, Tianhong Dai, Hengyan Liu^* (Corresponding Author)

^*Corresponding author for this work

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Abstract

Despite extensive research, remote sensing image classification remains a challenging issue within the field of remote sensing image analysis. Achieving a balance between classification accuracy and computational efficiency remains challenging, as traditional methods often face difficulties in attaining both high speed and precision simultaneously. To tackle this dilemma, we propose a method named IMVR which significantly reduces the computational burden while maintaining validity. This method enhances the richness and accuracy of high-dimensional feature representations through its output. Extensive experiments are conducted on the UC Merced Land-Use Dataset to demonstrate that our method can substantially improve classification performance and efficiency in comparison to traditional methods.

Original language	English
Title of host publication	Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	27-31
Number of pages	5
ISBN (Electronic)	9798350308693
DOIs	https://doi.org/10.1109/CyberC58899.2023.00015
Publication status	Published - 21 Feb 2024
Event	15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023 - Jiangsu, China Duration: 2 Nov 2023 → 4 Nov 2023

Conference

Conference	15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023
Country/Territory	China
City	Jiangsu
Period	2/11/23 → 4/11/23

Bibliographical note

We sincerely thank the Climatic Data Centre, part of the National Mete- orological Information Centre (CMA Meteorological Data Centre), for their invaluable assistance and cooperation in providing us with the meteorological data used in this study.

Keywords

deep learning
Feature classification
image classification
Remote sensing image classification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CyberC58899.2023.00015Licence: Unspecified

Cite this

Shuai, Y., Xin, N., Hasan, M. M., Hu, B., Dai, T., & Liu, H. (2024). Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets. In Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023 (pp. 27-31). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CyberC58899.2023.00015

Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets. / Shuai, Yuanzhen; Xin, Ning; Hasan, Md Maruf et al.
Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023. Institute of Electrical and Electronics Engineers Inc., 2024. p. 27-31.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Shuai, Y, Xin, N, Hasan, MM, Hu, B, Dai, T & Liu, H 2024, Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets. in Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023. Institute of Electrical and Electronics Engineers Inc., pp. 27-31, 15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023, Jiangsu, China, 2/11/23. https://doi.org/10.1109/CyberC58899.2023.00015

Shuai Y, Xin N, Hasan MM, Hu B, Dai T, Liu H. Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets. In Proceedings - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023. Institute of Electrical and Electronics Engineers Inc. 2024. p. 27-31 doi: 10.1109/CyberC58899.2023.00015

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abstract = "Despite extensive research, remote sensing image classification remains a challenging issue within the field of remote sensing image analysis. Achieving a balance between classification accuracy and computational efficiency remains challenging, as traditional methods often face difficulties in attaining both high speed and precision simultaneously. To tackle this dilemma, we propose a method named IMVR which significantly reduces the computational burden while maintaining validity. This method enhances the richness and accuracy of high-dimensional feature representations through its output. Extensive experiments are conducted on the UC Merced Land-Use Dataset to demonstrate that our method can substantially improve classification performance and efficiency in comparison to traditional methods.",

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AU - Dai, Tianhong

AU - Liu, Hengyan

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AB - Despite extensive research, remote sensing image classification remains a challenging issue within the field of remote sensing image analysis. Achieving a balance between classification accuracy and computational efficiency remains challenging, as traditional methods often face difficulties in attaining both high speed and precision simultaneously. To tackle this dilemma, we propose a method named IMVR which significantly reduces the computational burden while maintaining validity. This method enhances the richness and accuracy of high-dimensional feature representations through its output. Extensive experiments are conducted on the UC Merced Land-Use Dataset to demonstrate that our method can substantially improve classification performance and efficiency in comparison to traditional methods.

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Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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