%0 Journal Article %T Comparaison between the method which is used the spatial contextual information and some methods of image classification %A Hassouna, Houda %J Mathematics in Natural Science %D 2019 %V 5 %N 1 %@ ISSN 2600-7665 %F Hassouna2019 %X In this paper, we present the results obtained for the remote sensing image classification by using three methods of classification namely, Gaussian process classification method (GPC), morphological profile for classification method (MPC) and spatial contextual Gaussian process classification method (SGPC). Several classification approaches have shown that the exploitation of spatial contextual information can be attractive to increase the classification accuracy by introducing a new automated learning approach based on Gaussian process theory. %9 journal article %R 10.22436/mns.05.01.02 %U http://dx.doi.org/10.22436/mns.05.01.02 %P 13--19 %0 Journal Article %T Classification of Hyperspectral Remote Sensing Images Using Gaussian Processes %A Y. Bazi %A F. Melgani %J IEEE Trans. Geosci. Remote Sensing Symposium %D 2008 %V 2008 %F Bazi2008 %0 Journal Article %T Gaussian process approach to remote sensing image classification %A Y. Bazi %A F. Melgani %J IEEE Transactions on Geoscience and Remote Sensing %D 2009 %V 48 %F Bazi2009 %0 Journal Article %T Comparison between spatial contextual Gaussian process classification method and other methods %A H. Hassouna %A F. Melgani %J The conference ADAM (Errachidia, Morroco) %D 2017 %V 2017 %F Hassouna2017 %0 Journal Article %T Spatial Contextual Gaussian Process Learning for Remote Sensing Image Classification %A H. Hassouna %A F. Melgani %A Z. Mokhtari %J Remote Sensing Lett. %D 2015 %V 6 %F Hassouna2015 %0 Journal Article %T Spatially Adaptive Classication of Hyperspectral Data with Gaussian Processes %A G. Jun %A J. Ghosh %J in: IEEE International Geoscience and Remote Sensing Symposium %D 2009 %V 2 %F Jun2009 %0 Book %T Evaluation of Gaussian Processes and Other Methods for Non-Linear Regression %A C. E. Rasmussen %D 1997 %I Ph.D. thesis (University of Toronto), ProQuest LLC %C Ann Arbor %F Rasmussen1997 %0 Journal Article %T Infinite Mixtures of Gaussian Process Experts %A C. E. Rasmussen %A Z. Ghahramani %J Proceeding NIPS'01 Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic %D 2001 %V 2001 %F Rasmussen2001 %0 Journal Article %T Gaussian Processes in Reinforcement Learning %A C. E. Rasmussen %A M. Kuss %J Proceeding NIPS'03 Proceedings of the 16th International Conference on Neural Information Processing Systems %D 2003 %V 2003 %F Rasmussen2003 %0 Book %T Gaussian Process for Machine Learning %A C. E. Rusmassen %A C. K. I. Williams %D 2005 %I MIT Press %C Cambridge %F Rusmassen2005