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

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 for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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	2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)
Publisher	IEEE
Publication status	Published - 2023

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

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@inproceedings{35fc6a1e61a642d999a81f8509cd39c0,

title = "Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets",

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.",

author = "Yuanzhen Shuai and Ning Xin and Hasan, {Md Maruf} and Bintao Hu and Tianhong Dai and Hengyan Liu",

year = "2023",

language = "English",

booktitle = "2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)",

publisher = "IEEE",

}

Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets. / Shuai, Yuanzhen; Xin, Ning ; Hasan, Md Maruf et al.
2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2023.

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Application of IMVR Convolutional Neural Networks to Classification of Land Use Remote Sensing Datasets

AU - Shuai, Yuanzhen

AU - Xin, Ning

AU - Hasan, Md Maruf

AU - Hu, Bintao

AU - Dai, Tianhong

AU - Liu, Hengyan

PY - 2023

Y1 - 2023

N2 - 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.

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.

M3 - Conference Proceeding

BT - 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

PB - IEEE

ER -

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

Abstract

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