Quantum Machine Learning on Remote Sensing Data Classification
by Yi Liu1 
, Wendy Wang 1,* 
, Haibo Wang 3 , Bahram Alidaee4
1 University of Massachusetts Dartmouth, Department of Computer and Information Science, Dartmouth, MA 02747, USA
2 University of North Alabama, Computer Sciences and Information Systems, Florence, AL 35632, USA
3 Texas A&M International University, Division of International Business and Technology Studies, Laredo, TX 78041, USA
4 University of Mississippi, Department of Marketing, Oxford, MS 38677, USA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 2, Issue 12, Page # 23-33, 2023; DOI: 10.55708/js0212004
Keywords: Machine Learning, Remote Sensing Data Classification, Support Vector Machine, Classical Machine Learning
Received: 25 November 2023, Revised: 23 December 2023, Accepted: 24 December 2023, Published Online: 30 December 2023
APA Style
Liu, Y., Wang, W., Wang, H., & Alidaee, B. (2023). Quantum Machine Learning on Remote Sensing Data Classification. Journal of Engineering Research and Sciences, 2(12), 23–33. https://doi.org/10.55708/js0212004
Chicago/Turabian Style
Liu, Yi, Wendy Wang, Haibo Wang, and Bahram Alidaee. “Quantum Machine Learning on Remote Sensing Data Classification.” Journal of Engineering Research and Sciences 2, no. 12 (December 1, 2023): 23–33. https://doi.org/10.55708/js0212004.
IEEE Style
Y. Liu, W. Wang, H. Wang, and B. Alidaee, “Quantum Machine Learning on Remote Sensing Data Classification,” Journal of Engineering Research and Sciences, vol. 2, no. 12, pp. 23–33, Dec. 2023, doi: 10.55708/js0212004.
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Abstract
Information extracted from remote sensing data can be applied to monitor the business and natural environments of a geographic area. Although a wide range of classical machine learning techniques have been utilized to obtain such information, their performance differs greatly in classification accuracy. In this study, we aim to examine whether quantum-enhanced machine learning can improve the performance of classical machine learning algorithms in binary classifications of satellite remote sensing data. Using 16 pre-labeled datasets, we apply Support Vector Machine-quantum annealing solver (SVM-QA) – a type of quantum machine learning algorithm, with optimized (Gamma) value on the task of image classification and compare its results with the top performers of classical machine learning algorithms. The results show that in 10 out of 16 datasets, the hyper parameterized SVM-QA classifier outperforms the best classical machine learning algorithms in terms of classification accuracy. The findings suggest the potentiality of quantum computing in remote sensing. This study contributes to the literature of remote sensing image data classification and applications of quantum machine learning for problem solving.
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