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Classification of Remote Sensing Images with Parametrized Quantum Gates

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

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

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/143628/
Dokumentart:Zeitschriftenbeitrag
Titel:Classification of Remote Sensing Images with Parametrized Quantum Gates
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Otgonbaatar, SoronzonboldSoronzonbold.Otgonbaatar (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datcu, MihaiMihai.Datcu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Januar 2022
Erschienen in:IEEE Geoscience and Remote Sensing Letters
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:19
DOI:10.1109/LGRS.2021.3108014
Seitenbereich:Seite 8020105
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:veröffentlicht
Stichwörter:Parametrized Quantum Circuit, Quantum Machine Learning, Earth Observation
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Künstliche Intelligenz, R - Quantencomputing
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Otgonbaatar, Soronzonbold
Hinterlegt am:18 Okt 2021 10:21
Letzte Änderung:01 Mär 2023 03:00

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