Semisupervised classification of hyperspectral remote sensing images with spatial majority voting
Semisupervised learning is very useful for hyperspectral image classification, when the number of labeled samples is insufficient. In this paper, a simple selftraining method with spatial majority filtering is proposed to find unlabeled samples that can assist classifier training. It is based on the assumption that an unlabeled pixel, after initial classification, that has the same class label of neighboring pixels, is reliable. This results in majority voting (MV) based algorithm. The performance can be improved if the spectral similarity between a center and surrounding pixels is considered in weighted majority voting (MWV). Experiments with four real datasets confirm their effectiveness in improving classification accuracy when the number of labeled samples is very limited.