Semisupervised Discriminant Analysis for Hyperspectral Imagery With Block-Sparse Graph

In this letter, a semisupervised block-sparse graph is proposed for discriminant analysis of hyperspectral imagery. To overcome the difficulty of not having enough training samples in the previously developed block-sparse graph approach, unlabeled samples are selected to participate in graph construction. Both sparse and collaborative representations are used for unlabeled sample selection. The experimental results demonstrate that the proposed semisupervised block-sparse graph can significantly outperform the supervised version with limited training samples. The sparse and collaborative representation-based selection methods perform comparably with the collaborative version requiring much lower computational cost.