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In this paper, we propose a novel contextual classification of hyperspectral data. We use probabilistic label relaxation (PLR) process to incorporate context information into the spectral pixelwise classification procedure. In conventional PLR procedure, first a maximum likelihood classification is performed and class probabilities are computed by using multivariate normal models. However this method is not efficient for hyperspectral data with limited training samples. In this paper we suggest to use support vector machine (SVM) in order to initial classification and also use class probability estimates which are obtained from SVM classification for PLR postprocess. Experimental results are presented for an agricultural hyperspectral data. The proposed method improve dramatically classification accuracies, when compare to spectral pixelwise classification. Moreover our proposed method can improve performance of conventional PLR postprocess for hyperspectral data.