ARTIFICIAL BEE COLONY FOR CLASSIFICATION OF SATELLITE IMAGE OF ORAN CITY

Abstract

Remote sensing is widely used globally to better understand the Earth's surface and atmosphere. Among its applications, mapping land cover is fundamental. In this study, we focused on satellite image classification to address this issue, using the Artificial Bee Colony (ABC) Algorithm, a well-established optimization technique. Our project proposes an implementation of ABC for satellite image classification, evaluating performance using validity indices such as the Calinski-Harabasz Index (CHI) and the Davies-Bouldin Index (DBI). We apply the ABC algorithm to classify satellite images of Oran. This comparison allows us to assess the performance of the two validity indices in remote sensing image classification.