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000909756 1001_ $$0P:(DE-Juel1)191143$$aPasetto, Edoardo$$b0$$eCorresponding author
000909756 1112_ $$aIEEE International Geoscience and Remote Sensing Symposium (IGARSS)$$cKuala Lumpur$$d2022-07-17 - 2022-07-22$$gIGARSS 2022$$wMalaysia
000909756 245__ $$aQuantum Support Vector Regression for Biophysical Variable Estimation in Remote Sensing
000909756 260__ $$bIEEE$$c2022
000909756 300__ $$a4903-4906
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000909756 520__ $$aRegression 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.
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000909756 7001_ $$0P:(DE-Juel1)191384$$aDelilbasic, Amer$$b1
000909756 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b2
000909756 7001_ $$0P:(DE-Juel1)167543$$aWillsch, Madita$$b3
000909756 7001_ $$0P:(DE-HGF)0$$aMelgani, Farid$$b4
000909756 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b5
000909756 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b6
000909756 773__ $$a10.1109/IGARSS46834.2022.9883963
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