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Acta Scientific Computer Sciences

Research Article Volume 2 Issue 12

Application of the Pigeon Method to the Classification of Captured Data

Yasmine Benyettou* and Hadria Fizazi

University of Sciences and Technology of Oran, USTO, Algeria

*Corresponding Author: Yasmine Benyettou, University of Sciences and Technology of Oran, USTO, Algeria.

Received: September 24, 2020; Published: November 18, 2020



  This paper presents a method to increase the classification performance of satellite images by swarm intelligence. Traditional statistical classifiers have limitations in solving complex classification problems due to their harsh assumptions because these methods only examine spectral variance by ignoring the spatial distribution of pixels corresponding to land cover classes and the correlation between the different bands. An optimization algorithm inspired by the behavior of pigeons is applied and has been used in various fields such as image restoration, planning of the trajectories of aerial robots. In our case, the basic idea is: Davies-Bouldin (DBI) is used as a fitness function. The iterative optimization process is carried out by the pigeon optimization algorithm. In this process, the fitness function matches the coordinate of the pigeon in optimizing the problem. The best result is obtained when the pigeon finds the best overall position. This method converts the problem of finding the optimal solution to the problem of solving multidimensional variables and efficiently optimizes the result. In order to verify the feasibility and accuracy of the supervised classification, the K-means bisecting technique and the deep learning method were implemented. The results of the comparison indicate that the method based on the inspired pigeon optimization is effective with a good classification rate equal to 95.60%, an accuracy rate of 84.70% in a reduced execution time of 19.15 dry. The results of the calculation also show that the proposed PIO algorithm can effectively improve the speed of convergence, and the superiority of the overall search.

Keywords: Pigeon Inspired Optimization (PIO) Algorithm; K-Means Bisecting; Deep Learning; Satellite Image; Classification



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Citation: Yasmine Benyettou and Hadria Fizazi. “Application of the Pigeon Method to the Classification of Captured Data". Acta Scientific Computer Sciences 2.12 (2020): 27-35.


Acceptance rate35%
Acceptance to publication20-30 days

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