Semisupervised classification of hyperspectral images based on tri-training algorithm with enhanced diversity

Hyperspectral image classification faces a serious challenge due to the high dimension of hyperspectral data and limited labeled samples. Tri-training algorithm is a widely used semisupervised classification method, but the algorithm lacks significant diversity among the classifiers when the number of initial label samples is limited. A semisupervised classification method for hyperspectral data based on tri-training is proposed. It combines different classifiers and stratified sampling based on labeled class to increase classifier diversity and avoid classifier performance deterioration. Performance comparison between the proposed algorithm and tri-training algorithm was made through experiments. The proposed algorithm improved the overall accuracy and Kappa coefficient by 1.37% to 6.84% and 0.0096 to 0.0808, respectively, and the results showed that the effectiveness of the algorithm is verified. Moreover, the algorithm can also get better performance when the number of samples is small.