LAUSR

This letter proposes a semi-supervised mixtures of factor analyzers (S2MFA) feature extraction (FE) method for hyperspectral image (HSI). S2MFA uses a Gaussian mixture model to segment the image to different regions, each region follows a Gaussian distribution and contains labeled and unlabeled samples. The method uses a factor analyzer to get a factor-loading matrix to preserve the local spatial information using the labeled and unlabeled samples. It simultaneously improves the class discrimination of the data using the labeled samples and also transforms the original image to an optimal low-dimensional subspace to achieve dimensionality reduction. The performance of the S2MFA FE method is evaluated by classification of two real HSIs and compared to different kinds of statistic unsupervised, supervised, and semi-supervised FE methods.