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Classification of Remote Sensing Images with Parametrized Quantum Gates

Otgonbaatar, Soronzonbold and Datcu, Mihai (2022) Classification of Remote Sensing Images with Parametrized Quantum Gates. IEEE Geoscience and Remote Sensing Letters, 19, p. 8020105. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2021.3108014. ISSN 1545-598X.

[img] PDF - Only accessible within DLR bis February 2023 - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/document/9531639


This paper studies how to program and assess a Parametrized Quantum Circuit (PQC) for classifying Earth observation (EO) satellite images. In this exploratory study, we assess a PQC for classifying a two-label EO image dataset and compare it with a classic deep learning classifier. We use the PQC with an input space of only 17 quantum bits (qubits) due to the current limitations of quantum technology. As a realworld image for EO, we selected the Eurosat dataset obtained from multispectral Sentinel-2 images as a training dataset and a Sentinel-2 image of Berlin, Germany as a test image. However, the high dimensionality of our images is incompatible with the PQC input domain of 17 qubits. Hence, we had to reduce the dimensionality of the input images for this two-label case to a vector with 16 elements; the 17th qubit remains reserved for storing label information. We employed a Very Deep Convolutional Network with an autoencoder as a technique for the dimensionality reduction of the input image, and we mapped the dimensionally-reduced image onto 16 qubits by means of parameter thresholding. Then, we used a PQC to classify the twolabel content of the dimensionally-reduced Eurosat image dataset. A PQC classifies the Eurosat images with high accuracy as a classic deep learning method (and with even better accuracy in some instances). From our experiment, we derived and enhanced deeper insight into programming future gate-based quantum computers for many practical problems in EO.

Item URL in elib:https://elib.dlr.de/143628/
Document Type:Article
Title:Classification of Remote Sensing Images with Parametrized Quantum Gates
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Otgonbaatar, SoronzonboldSoronzonbold.Otgonbaatar (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:January 2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2021.3108014
Page Range:p. 8020105
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Parametrized Quantum Circuit, Quantum Machine Learning, Earth Observation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence, R - Quantum computing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Otgonbaatar, Soronzonbold
Deposited On:18 Oct 2021 10:21
Last Modified:14 Jan 2022 15:22

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