Land Cover Classification Using Extremely Randomized Trees: A Kernel Perspective

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.