Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification

Abstract

Dimensionality Reduction (DR) models are highly useful for tackling Hyperspectral Images (HSIs) classification tasks. They mainly address two issues: the curse of dimensionality with respect to spectral features, and the limited number of labeled training samples. Among these DR techniques, the Graph-Embedding Discriminant Analysis (GEDA) framework has demonstrated its effectiveness for HSIs feature extraction. However, most of the existing GEDA-based DR methods largely rely on manually tuning the parameters so as to obtain the optimal model, which proves to be troublesome and inefficient. Motivated by the nonparametric Gaussian Process (GP) model, we propose a novel supervised DR algorithm, namely Gaussian Process Graph-based Discriminate Analysis (GPGDA). Our algorithm takes full advantage of the covariance matrix in GP to constructing the graph similarity matrix in GEDA framework. In this way, more superior performance can be provided with the model parameters tuned automatically. Experiments on three real HSIs datasets demonstrate that the proposed GPGDA outperforms some classic and state-of-the-art DR methods.