In this paper a nonparametric contextual classification using both spectral and spatial information will be proposed for hyperspectral image classification. Essentially, among the classification, spatial information is acquired on the basis of Markov random field (MRF) and then joined with the nonparametric density estimation. Two MRF-based nonparametric contextual classifications based on kNN and Parzen density estimation will be introduced. We expect this combination could strengthen the capability for classifying pixels of different class labels with similar spectral values and dealing with data that has no clear numerical interpretation.