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Turkish Journal of Electrical Engineering and Computer Sciences

DOI

10.55730/1300-0632.4029

Abstract

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to evade falling into the local optimum by creating a balance between exploration and exploitation strategies to accelerate convergence toward the global optimum solution. Finally, two machine learning classifiers (knearest neighbor and support vector machine) were employed for hyperspectral image classification at the pixel level. Moreover, the proposed method was compared with the bat algorithm, Archimedes optimization algorithm, particle swarm optimization, standard harmony search, genetic algorithm, and krill herd algorithm. The experimental results demonstrated significant improvement with overall accuracy equal to 87.49%, 94.85%, and 94.41% for the Indian Pines, Pavia University, and Salinas datasets, respectively.

Keywords

Metaheuristic algorithm, exploration, exploitation, band selection, fitness function

First Page

969

Last Page

991

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