Combine labeled and unlabeled information for hyperspectral image classification

In hyperspectral image classification, semisupervised learning can be applied when labeled samples are limited. By utilizing unlabeled information, classification accuracy generally can be improved. Graph-based regularization is a widely used semisupervised learning technique, where graph construction with both labeled and unlabeled samples is very computationally expensive. In reality, samples are highly correlated; so it may be unnecessary to use all the unlabeled samples. Appropriate selection of unlabeled samples can not only help improve classification but also significantly reduce the computational cost. In this paper, we propose an unlabeled sample selection algorithm. The preliminary result from a semisupervised graph-regularized kernel classifier demonstrates its effectiveness.