Spatial contextual classification of remote sensing images using a Gaussian process

ABSTRACT

Gaussian process (GP) classifiers represent a powerful and interesting theoretical framework for the Bayesian classification of remote sensing images. However, the integration of spatial information in GP classifier is still an open question, while researches have demonstrated that the classification results could be improved when the spatial information is used. In this context, in order to improve the performance of the traditional GP classifier, we propose to use Markov random fields (MRFs) to refine the classification results with the neighbourhood information in the images. In the proposed method (denoted as GP-MRF), the MRF model is used as a post-processing step to the pixelwise results with GP classifier which classifies each pixel in the image separately. Therefore, the proposed GP-MRF approach promotes solutions in which adjacent pixels are likely to belong to the same class. Experimental results show that the GP-MRF could achieve better classification accuracy compared to the original GP classifier and the state-of-the-art spatial contextual classification methods.

Additional information

Funding

This research was conducted with support of the Natural Science Foundation of China under Grants [61271439, 61301235, and 61372159], the Hunan Provincial NSF of China under Grant [2015JJ3018], the Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China under Grant [201243], and Program for New Century Excellent Talents in University under Grant [NECT-13-0164].