Investigating the effects of ensemble classification on remotely sensed data for land cover mapping

Ensemble classification involves consulting experts in taking final decision in classification process. The idea is to improve classification accuracy when compared to their single classifier counterpart. The system is used in remote sensing imagery to obtain information about Land cover. Major challenges associated with classification accuracy include design procedure of classifier, choice of training sets from dataset and information conveyed to the algorithm. Superiority of different classification approaches employed depends on selected dataset and the strategy used during designing phase of each classifier. However, in ensemble approach, there is no definite number of classifiers that should take part in decision making. This study exploits feature selection technique to create diversity in ensemble classification. Results obtained show that for ensemble approach, there is no significant benefit in having many base classifiers. The outcome should reveal how to design best ensemble using feature selection approach for land cover mapping.