Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Gabriele Cavallaro; Dennis Willsch; Madita Willsch; Kristel Michielsen; Morris Riedel

doi:10.1109/IGARSS39084.2020.9323544

Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Gabriele Cavallaro, Dennis Willsch, Madita Willsch, Kristel Michielsen, Morris Riedel

Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Support Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1973-1976
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323544
Publication status	Published - 26 Sept 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: 26 Sept 2020 → 2 Oct 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	26/09/20 → 2/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Other keywords

classification
multispectral image
quantum annealing
Quantum computation
remote sensing
support vector machine

Access to Document

10.1109/IGARSS39084.2020.9323544

Cite this

Cavallaro, G., Willsch, D., Willsch, M., Michielsen, K., & Riedel, M. (2020). Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1973-1976). Article 9323544 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9323544

Cavallaro, Gabriele ; Willsch, Dennis ; Willsch, Madita et al. / Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1973-1976 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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abstract = "Support Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.",

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Cavallaro, G, Willsch, D, Willsch, M, Michielsen, K & Riedel, M 2020, Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9323544, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 1973-1976, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 26/09/20. https://doi.org/10.1109/IGARSS39084.2020.9323544

Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer. / Cavallaro, Gabriele; Willsch, Dennis; Willsch, Madita et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1973-1976 9323544 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1973-1976. 9323544. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9323544

Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer

Abstract

Publication series

Conference

Bibliographical note

Other keywords

Access to Document

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Cite this