Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

Fan Fan; Yilei Shi; Xiao Xiang Zhu

doi:10.1109/IGARSS46834.2022.9883949

Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

Fan Fan, Yilei Shi, Xiao Xiang Zhu

Chair of Data Science in Earth Observation

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

3 Scopus citations

Abstract

Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.

Original language	English
Title of host publication	IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	191-194
Number of pages	4
ISBN (Electronic)	9781665427920
DOIs	https://doi.org/10.1109/IGARSS46834.2022.9883949
State	Published - 2022
Event	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2022-July

Conference

Conference	2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/07/22 → 22/07/22

Keywords

Earth Observation
Image Classification
Quantum Circuit
Quantum Machine Learning

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10.1109/IGARSS46834.2022.9883949

Cite this

Fan, F., Shi, Y., & Zhu, X. X. (2022). Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 191-194). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883949

@inproceedings{2c596f64a3bc4864a16344bcbbcff8fa,

title = "Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network",

abstract = "Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.",

keywords = "Earth Observation, Image Classification, Quantum Circuit, Quantum Machine Learning",

author = "Fan Fan and Yilei Shi and Zhu, {Xiao Xiang}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.1109/IGARSS46834.2022.9883949",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "191--194",

booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",

}

Fan, F, Shi, Y & Zhu, XX 2022, Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network. in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 191-194, 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, Kuala Lumpur, Malaysia, 17/07/22. https://doi.org/10.1109/IGARSS46834.2022.9883949

Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network. / Fan, Fan; Shi, Yilei; Zhu, Xiao Xiang.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc., 2022. p. 191-194 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July).

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

TY - GEN

T1 - Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

AU - Fan, Fan

AU - Shi, Yilei

AU - Zhu, Xiao Xiang

PY - 2022

Y1 - 2022

N2 - Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.

AB - Due to the rapid growth of earth observation (EO) data and the complexity of machine learning models, the high requirement on the computation power for EO data analysis becomes a bottleneck. Exploiting quantum computing might tackle this challenge in the future. In this paper, we present a hybrid quantum-classical convolutional neural network (QC-CNN) to classify EO data which can accelerate feature extraction compared with its classical counterpart and handle multi-category classification tasks with reduced quantum resources. The model's validity is verified with the Overhead-MNIST dataset through the TensorFlow Quantum platform.

KW - Earth Observation

KW - Image Classification

KW - Quantum Circuit

KW - Quantum Machine Learning

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U2 - 10.1109/IGARSS46834.2022.9883949

DO - 10.1109/IGARSS46834.2022.9883949

M3 - Conference contribution

AN - SCOPUS:85140403816

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 191

EP - 194

BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022

Y2 - 17 July 2022 through 22 July 2022

ER -

Fan F, Shi Y, Zhu XX. Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. Institute of Electrical and Electronics Engineers Inc. 2022. p. 191-194. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS46834.2022.9883949

Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

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