Alsat-2B/Sentinel-2 Imagery Classification Using the Hybrid Pigeon Inspired Optimization Algorithm
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초록
Classification is a substantial operation in data mining, and each element is distributed taking into account itsfeature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satelliteimage classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimizationalgorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into thepigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a seriesof images, supported by computational results that demonstrate the cogency of the proposed classificationmethod on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO isapplied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experimentbetween FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GAoutperformed FPIO in matters of time computing. However, FPIO provided better quality results with lessconfusion. The overall experimental results demonstrate that the proposed approach is an efficient method forsatellite imagery classification.
Abstract
Classification is a substantial operation in data mining, and each element is distributed taking into account itsfeature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satelliteimage classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimizationalgorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into thepigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a seriesof images, supported by computational results that demonstrate the cogency of the proposed classificationmethod on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO isapplied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experimentbetween FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GAoutperformed FPIO in matters of time computing. However, FPIO provided better quality results with lessconfusion. The overall experimental results demonstrate that the proposed approach is an efficient method forsatellite imagery classification.
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