Effect of Feature Dimensionality on Object-based Land Cover Classification: A Comparison of Three Classifiers

Abstract

The efficient mapping of land cover from remotely sensed data is highly desirable as land cover information is essential for a range of environmental and socio-economic applications. Supervised classifiers are often applied in remote sensing to extract land cover information. While spectral information is typically used as the main discriminating features for such classifiers, additional features such as vegetation indices, transformed spectral data, textural information, contextual information and ancillary data may also considerably influence the accuracy of classification. Geographic object-based image analysis (GEOBIA) allows the easy integration of such additional features into the classification process. This paper compares the performance of three supervised classifiers in a GEOBIA environment as an increasing number of object features are included as classification input. Classification tree analysis (CTA) was employed for feature selection and importance ranking. Object features were considered in the order of their obtained rank. The support vector machine (SVM) produced superior classification accuracies when compared to those of nearest neighbour (NN) and maximum likelihood (ML) classifiers. Both SVM and NN produced stable results as the feature-set size was increased towards the maximum (22 features). ML’s performance, however, decreased considerably when few training samples are used and when the feature-set size (dimensionality) is increased.