Integration of Gaussian process and MRF for hyperspectral image classification

Wentong Liao, Jun Tang, Bodo Rosenhahn, Micheal Ying Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


In this paper, we propose a framework GP-MRF, which combines Gaussian processes (GPs) and Markov random field (MRF) for accurate classification of hyperspectral remote sensing image (HSI) data. This method exploits the relationship among adjacent pixels and integrates it into spectral information to obtain spectral-spatial classification. This framework consists of two steps. Firstly, a GP classifier (GPC) yields pixelwise predictive probability for each class. Secondly, an MRF is applied to extract spatial contextual information in the label map achieved in the first step. Then the classification results are inferred from the spectral-spatial information. By means of MRF regularization an enhanced classification result has been obtained. The experiments are performed on three hyperspectral benchmark datasets. The results from the GPC are compared with those obtained by state-of-the-art classification approaches and demonstrate that, GP model is a competitive tool for classification of HSI in terms of accuracy. Furthermore, the experimental results indicate that our proposed method GP-MRF improves the classification accuracy of conventional GPC.

Original languageEnglish
Title of host publicationJoint Urban Remote Sensing Event, JURSE 2015
Place of PublicationLausanne
Number of pages4
ISBN (Electronic)9781479966523
Publication statusPublished - 9 Jun 2015
Externally publishedYes
EventJoint Urban Remote Sensing Event, JURSE 2015 - Lausanne, Switzerland
Duration: 30 Mar 20151 Apr 2015

Publication series

Name2015 Joint Urban Remote Sensing Event, JURSE 2015


ConferenceJoint Urban Remote Sensing Event, JURSE 2015
Abbreviated titleJURSE 2015
Internet address


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