Journal of Information Processing Systems

  • 제17권4호
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  • Pages.690-706
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  • 2021
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm

  • Arezki, Dounia (Laboratory Signal Image Parole (SIMPA), University of Science and Technology of Oran Mohamed-Boudiaf) ;
  • Fizazi, Hadria (Laboratory Signal Image Parole (SIMPA), University of Science and Technology of Oran Mohamed-Boudiaf)
  • 투고 : 2020.09.15
  • 심사 : 2021.05.23
  • 발행 : 2021.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

키워드

과제정보

This paper is supported by the University of Science and Technology of Oran Mohamed-Boudiaf, Algeria.

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