Discriminative Pixel-Pairwise Constraint-Guided Extreme Learning Machine for Semi-Supervised Hyperspectral Image Classification

Generally, the traditional semi-supervised extreme learning machine (S²-ELM) method cannot fully exploit the limited label information in hyperspectral image (HSI) classification. In this paper, we propose a discriminative S²-ELM method, called pixel-pairwise constrained S²-ELM (P²S²- ELM) method. Both the manifold regularization to leverage unlabeled data and the pixel-pairwise constraint between the labeled pixels are incorporated into a unified minimizing framework, thus the proposed P²S²-ELM method is able to learn a more effective and discriminative projection. Experimental results on several real hyperspectral data sets exhibit its efficiency and superiority to the counterparts, when only a small number of labeled samples are available.