Supervised Gaussian Process Latent Variable Model for Hyperspectral Image Classification

Abstract

Discriminative features are significant for hyper-spectral image (HSI) classification. In this letter, we apply the supervised dimensionality reduction (DR) model termed supervised latent linear Gaussian process latent variable model (SLLGPLVM) for feature extraction. As a semiparametric classification model, the new model has ability in simultaneous feature extraction and classification and demonstrates high classification accuracy with only a small training set. This is therefore suitable for HSI classification. Experimental results on six real HSI data sets show that the proposed SLLGPLVM outperforms several conventional supervised DR models and the support vector machine implemented in the original spectral space.