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Metaheuristics and Neural Network for Satellite Images Classification

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This article presents a comparative study between Metaheuristics and Multi-Layer Perceptron (MLP) neural network. These techniques are used to classify the pixels of satellite images, allowing to divide an image into its constituent classes. This study has used two metaheuristics, the first one called Electromagnetic Metaheuristic (EM) inspired by physics, which simulate the movement of charged particles. The second metaheuristic named API, belongs to the ant colony optimization (ACO), and it is inspired by the behaviour of a real ant colony. The Multi-Layer Perceptron inspired by the human brain mechanism is used in this research as well. To evaluate the performance of each method the overall accuracy (OA) and the Kappa coefficient (KC) of the classification results have been calculated. The experimental results show that metaheuristics are better than the MLP neural network. Actually, there is a significant difference in the OA of 4.37% and an improvement in KC of 0.0582.
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Image Classification; Ant Colony Optimization; Electromagnetic Metaheuristic; Neural Network; Satellite Images

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