A novel study of artificial bee colony with clustering technique on paddy rice image classification

Abstract

Rice is a crop of global importance. To predict the area of paddy rice and thus its production, it draws great attraction of using data mining approaches on remote sensing data, which are well accepted. Many approaches based on supervised and unsupervised learning techniques have been developed over the years. Artificial bee colony (ABC) algorithm with a clustering technique is one of the most popular swarm-based algorithms. In this study, ABC algorithm is used to perform the rice image classification based on remote sensing imagery. This study comprises two stages. In the first part of the study, the ancillary information composed from the original spectra is applied to increase the performance of classification. As the other parts of the study, an efficient unsupervised classifier is developed to evaluate the performance of the incorporated ancillary information. This study integrates the ABC algorithm into a clustering process to build a land cover classifier system. On the other hand, a parallel approach using ant colony optimization (ACO) is studied for comparison. Two significant contributions are presented in this study: (1) a paddy rice image classifier is built with ABC algorithm and (2) the outcome of classifier using ABC algorithm outperforms that using ACO algorithm.