Land Cover Classification Using Extremely Randomized Trees: A Kernel Perspective

Abstract

The classification of the ever-increasing collections of remotely sensed images is a key but challenging task. In this letter, we introduce the use of extremely randomized trees known as Extra-Trees (ET) to create a similarity kernel [ET kernel (ETK)] that is subsequently used in a support vector machine (SVM) to create a novel classifier. The performance of this classifier is benchmarked against that of a standard ET, an SVM with both conventional radial basis function (RBF) kernel, and a recently introduced random forest-based kernel (RFK). A time series of Worldview-2 images over smallholder farms is used to illustrate our study. Four sets of features were obtained from these images by extending their original spectral bands with vegetation indices and textures derived from gray-level co-occurrence matrices. This allows testing the performance of the classifiers in low- and high-dimensional problems. Our results for the high-dimensional experiments show that the SVM with tree-based kernels provide better overall accuracies than with the RBF kernel. For problems with lower dimensionality, SVM-ETK slightly outperforms SVM-RFK and SVM-RBF. Moreover, SVM-ETK outperforms ET in most of the experiments. Besides an improved overall accuracy, the main advantage of ETK is its relatively low computational cost compared to the parameterization of the RBF and RFK. Thus, the proposed SVM-ETK classifier is an efficient alternative to common classifiers, especially in problems involving high-dimensional data sets.