Orthogonal Polynomials in the Problems of Digital Information Processing

Pyanylo, Yaroslav; Sobko, Valentyna; Pyanylo, Halyna; Pyanylo, Oksana

doi:10.55708/js0205001

Cited by 1 publication

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References 10 publications

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“…Remote sensing datasets play an important role in many big data applications, e.g., spatial analysis, earth observation modeling, urban planning, and prompt response to rapid changes in demographic, economic, and technological landscapes [1][2][3][4]. Massive geospatial data have been collected from a wide range of sources, such as satellites [5], mobile devices [6], and aerial photography [7] etc.…”

Section: Introductionmentioning

confidence: 99%

Quantum Machine Learning on Remote Sensing Data Classification

Liu,

Wang,

Wang

et al. 2023

J. Engg. Res. & Sci.

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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 Machinequantum 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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