Feature Selection via Cramer's V-Test Discretization for Remote-Sensing Image Classification

Abstract

A feature selection method based on Cramer's V-test (CV-test) discretization is presented to improve the classification accuracy of remotely sensed imagery. Three possible contributions are pursued in this paper. First of all, a Cramer's V-based discretization (CVD) algorithm is proposed to optimally partition the continuous features into discrete ones. Two association-based feature selection indexes, the CVD-based association index (CVDAI) and the class-attribution interdependence maximization (CAIM)-based association index (CAIMAI), derived from the CV-test value, are then proposed to select the optimal feature subset. Finally, the benefit of using discretized features to improve the performance with the J48 decision tree (J48-DT) and naive Bayes (NB) classifiers is studied. To validate the proposed approaches, a high spatial resolution image and two hyperspectral data sets were used to evaluate the performances of CVD and the associated algorithms. The test performances of discretization using CVD and two other state-of -the-art methods, the CAIM and equal width, show that the CVD-based technique has the better ability to generate a good discretization scheme. Furthermore, the feature selection indexes, CVDAI and CAIMAI, perform better than the other used feature selection methods in terms of overall accuracies achieved by the J48-DT, NB, and support vector machine classifiers. Our tests also show that the use of discretized features benefits the J48-DT and NB classifiers.