Semi-supervised dimensionality reduction for hyperspectral remote sensing image classification

Class labels and pairwise constraints are adopted as the prior information to present the semi-supervised dimensionality reduction for hyperspectral image. In this paper, we extend semi-supervised probabilistic principal component analysis (S
²
PPCA), semi-supervised local fisher discriminant analysis (S
²
LFDA) and semi-supervised dimensionality reduction with pairwise constraints (S
²
DRpc) to extract the features of hyperspectral image. These semi-supervised dimensionality reduction approaches are compared with PCA in classification task. Experimental results show that semi-supervised algorithms of S
²
PPCA and S
²
DRpc are superior to PCA.