Unlabeled Sample Reduction in Semi-supervised Graph-Based Band Selection for Hyperspectral Image Classification

Semi-supervised graph-based band selection methods have shown satisfying performances to choose the valuable bands for the hyper spectral data classification in case of very limited labeled samples. However, the calculation of adjacency matrices based on all labeled and unlabeled samples requires a large computational load which can be unacceptable with the huge amounts of unlabeled samples available. To address the problem, an unlabeled sample reduction method is proposed. The method involves dimensional reduction through PCA, over-segmentation through watershed, random sample selection from the resulting clusters. The band selection and classification experiments on hyper spectral data demonstrate that the proposed method can help improve the computational efficiency and performances of the graph-based algorithms by choosing the representative samples.