Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

Fan Fan, Yilei Shi, Xiao Xiang Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 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 languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages191-194
Number of pages4
ISBN (Electronic)9781665427920
DOIs
StatePublished - 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/07/2222/07/22

Keywords

  • Earth Observation
  • Image Classification
  • Quantum Circuit
  • Quantum Machine Learning

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