The Impact of Feature Selection on Urban Land Cover Classification

Abstract

Many of the studies in the literature about land cover classification are focused on the feature extraction and classification rather than feature selection. In this paper, the impact of feature selection on urban land cover classification is extensively analyzed. Three types of features namely spectral, texture, and size/shape features are used for this analysis. This analysis is carried out using three variations of a filter based feature selection method and three widely-known classification algorithms. The feature selection method used for the comparison is a multivariate filter method namely correlation-based feature subset selection where a feature subset evaluator and a search method are integrated. Best first search, genetic search, and greedy stepwise search are three different search methods used for this integration. The classification algorithms employed are Bayesian network, random forest, and support vector machine. The experimental results explicitly indicate that feature selection improves classification accuracy in all cases. Besides, according to the experimental results, random forest classifier is the most successful one among these three classifiers while both feature selection is applied and not applied. Largest improvement in the classification performance is obtained when greedy stepwise search based feature selection method and support vector machine classifier is applied together. Also, the contribution of spectral features to the performance of classification is more than size/shape and texture features

Many of the studies in the literature about land cover classification are focused on the feature extraction and classification rather than feature selection. In this paper, the impact of feature selection on urban land cover classification is extensively analyzed. Three types of features namely spectral, texture, and size/shape features are used for this analysis. This analysis is carried out using three variations of a filter based feature selection method and three widely-known classification algorithms. The feature selection method used for the comparison is a multivariate filter method namely correlation-based feature subset selection where a feature subset evaluator and a search method are integrated. Best first search, genetic search, and greedy stepwise search are three different search methods used for this integration. The classification algorithms employed are Bayesian network, random forest, and support vector machine. The experimental results explicitly indicate that feature selection improves classification accuracy in all cases. Besides, according to the experimental results, random forest classifier is the most successful one among these three classifiers while both feature selection is applied and not applied. Largest improvement in the classification performance is obtained when greedy stepwise search based feature selection method and support vector machine classifier is applied together. Also, the contribution of spectral features to the performance of classification is more than size/shape and texture features