This title appears in the Scientific Report :
2022
Please use the identifier:
http://hdl.handle.net/2128/32041 in citations.
Please use the identifier: http://dx.doi.org/10.1109/IGARSS46834.2022.9883963 in citations.
Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing
Quantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing
Regression analysis has a crucial role in many Earth Observation (EO) applications. The increasing availability and recent development of new computing technologies motivate further research to expand the capabilities and enhance the performance of data analysis algorithms. In this paper, the biophy...
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Personal Name(s): | Pasetto, Edoardo (Corresponding author) |
---|---|
Delilbasic, Amer / Cavallaro, Gabriele / Willsch, Madita / Melgani, Farid / Riedel, Morris / Michielsen, Kristel | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
IEEE
2022
|
Physical Description: |
4903-4906 |
ISBN: |
978-1-6654-2792-0 |
DOI: |
10.1109/IGARSS46834.2022.9883963 |
Conference: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur (Malaysia), 2022-07-17 - 2022-07-22 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1109/IGARSS46834.2022.9883963 in citations.
Regression analysis has a crucial role in many Earth Observation (EO) applications. The increasing availability and recent development of new computing technologies motivate further research to expand the capabilities and enhance the performance of data analysis algorithms. In this paper, the biophysical variable estimation problem is addressed. A novel approach is proposed, which consists in a reformulated Support Vector Regression (SVR) and leverages Quantum Annealing (QA). In particular, the SVR optimization problem is reframed to a Quadratic Unconstrained Binary Optimization (QUBO) problem. The algorithm is then tested on the D-Wave Advantage quantum annealer. The experiments presented in this paper show good results, despite current hardware limitations, suggesting that this approach is viable and has great potential. |