Quantum-DL Integration for Precise Remote Sensing Image Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-69986-3_34
Reference9 articles.
1. Sebastianelli, A., Zaidenberg, D.A., Spiller, D., Saux, B.L., Ullo, S.L.: On circuit-based hybrid quantum neural networks for remote sensing imagery classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 565–580 (2022). https://doi.org/10.1109/JSTARS.2021.3134785
2. Otgonbaatar, S., Datcu, M.: Classification of remote sensing images with parameterized quantum gates. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2021.3108014. Art no. 8020105
3. ,Gawron P., Lewiński, S.: Multi-spectral image classification with quantum neural network. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, pp. 3513–3516 (2020). https://doi.org/10.1109/IGARSS39084.2020.9323065
4. Cavallaro, G., Willsch, D., Willsch, M., Michielsen, K., Riedel, M.: Approaching remote sensing image classification with ensembles of support vector machines on the D-wave quantum annealer. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, pp. 1973–1976 (2020). https://doi.org/10.1109/IGARSS39084.2020.9323544
5. Broughton, M., et al.: Tensorflow quantum: a software framework for quantum machine learning. arXiv:2003.02989 (2020)
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