Semisupervised Remote Sensing Image Classification With Cluster Kernels

A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

I. Introduction

The problem of remote sensing image classification is very challenging given the typically low rate of labeled pixels per spectral band. Supervised classifiers such as support vector machines (SVMs) [1] excel in using the labeled information and have demonstrated very good performance in multispectral, hyperspectral, and multisource image classification [2]–[4]. However, when little labeled information is available, the underlying probability distribution function of the image is not properly captured, and a risk of poor generalization certainly exists. Modeling the data structure exploiting the information contained in unlabeled pixels can be done with semisupervised learning (SSL) methods, but in this case, the SVM classifier needs to be reformulated.