PPSPG: Label and Discriminant Feature Information Induced Superpixel Graph for Hyperspectral Image Classification

Graph-based methods have excellent performance in hyperspectral image (HSI) classification because of their strong ability to explore the relationship between labeled and unlabeled samples. However, most graph-based methods do not take sufficient account of label information and more discriminant features to establish graph connections, which will lead to a lot of improper connections, and then produce over-smooth or noisy classification results. To solve this issue, we propose a posterior probability fused superpixel graph (PPSPG), which exploits the fitting ability of supervised models to encode information from labels and discriminant features into the posterior probabilities. By measuring the weights between the posterior probabilities of any two superpixels, the proposed PPSPG can fully integrate label information into graph connetions, and alleviate the over-smoothing between superpixels. Experiments on real hyperspectral data show that our method has outstanding accuracies and can obtain quite distinct classification boundaries.