The DOI matches its Crossref record; 6 of the 7 references it cites were found in a registry or an index.
Multi-Spectral Image Classification with Quantum Neural Network
Abstract
Processing Earth observation images to obtain land cover classification is an important task allowing to track changes on the Earth's surface resulting from natural processes, human activity, and climate change. The amount of data acquired from Earth observation satellites is very large and their processing takes large amount of computational resources. We investigate application of quantum circuit based neural network classifiers for multi-spectral data classification aimed at obtaining the land cover information. We show a proof-of-concept of our approach.
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“It is worth to highlight that only very few works have addressed the application of quantum machine learning (QML) to remote sensing in the current state of the art.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [24]
Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Circuit-centric quantum classifiers
Schuld, M., Bocharov, A., Svore, K. M., & Wiebe, N. (2020) · Physical Review A · doi:10.1103/physreva.101.032308 · Cited by 1,057
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S., Ajith, V., Alam, M. S., Alonso-Linaje, G., AkashNarayanan, B., Asadi, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1811.04968 · Cited by 732
SENTINEL-2 SEN2COR: L2A Processor for Users
Louis, J., Debaecker, V., Pflug, B. M., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E. G., & Gascon, F. (2016) · elib (German Aerospace Center) · Cited by 175
Aggregation of Sentinel-2 time series classifications as a solution for multitemporal analysis
Nowakowski, A., Rybicki, M., Gromny, E., Malinowski, R., Krupiński, M., & Lewiński, S. (2017) · Image and signal processing for remote sensing. Conference · doi:10.1117/12.2277976 · Cited by 15
Post-processing tools for land cover classification of Sentinel-2
Gromny, E., Lewiński, S., Rybicki, M., Malinowski, R., Krupiński, M., & Nowakowski, A. (2019) · Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA) · doi:10.1117/12.2537325 · Cited by 1
Cited by
Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities
Zhang, L., & Zhang, L. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3145854 · Cited by 647
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162
Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification
Fan, F., Shi, Y., Guggemos, T., & Zhu, X. X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3312170 · Cited by 162
A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification
Delilbasic, A., Saux, B. L., Riedel, M., Michielsen, K. F. L., & Cavallaro, G. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3336926 · Cited by 49
Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment
Otgonbaatar, S., & Kranzlmüller, D. A. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3338970 · Cited by 31
Metadata
- Authors
- Gawron, P., & Lewinski, S.
- Affiliations
- Nicolaus Copernicus Astronomical Center (PL)
- Space Research Center, Polish Academy of Sciences,Warsaw,Poland,00-716
- ORCID
- Gawron, Piotr
- Venue
- IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
- Publisher
- IEEE
- Published
- 2020-09-26
- Pages
- 3513-3516
- OpenAlex
- W3131862063
- Cited by
- 42
- References
- 7
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 9 · 2023: 13 · 2022: 6 · 2021: 2
- FWCI
- 2.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (publishedVersion)
- Funders
- ERDF
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- quantum neural networks, land cover classification, Earth observation images, satellite imagery, quantum machine learning
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- Gawron, P., & Lewinski, S. (2020). Multi-Spectral Image Classification with Quantum Neural Network. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss39084.2020.9323065
