Next Article in Journal
TCANet for Domain Adaptation of Hyperspectral Images
Previous Article in Journal
PolSAR-Decomposition-Based Extended Water Cloud Modeling for Forest Aboveground Biomass Estimation
Open AccessArticle

Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification

1
School of Computer Science, China University of Geosciences, Wuhan 430074, China
2
Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Sydney 2006, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2288; https://doi.org/10.3390/rs11192288
Received: 21 August 2019 / Revised: 24 September 2019 / Accepted: 26 September 2019 / Published: 30 September 2019
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: hyperspectral image; dimensionality reduction; discriminant analysis; graph embedding; gaussian process hyperspectral image; dimensionality reduction; discriminant analysis; graph embedding; gaussian process
Show Figures

Graphical abstract

MDPI and ACS Style

Song, X.; Jiang, X.; Gao, J.; Cai, Z. Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification. Remote Sens. 2019, 11, 2288.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop