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Hyperspectral imagery (HSI) is now in use for a wide range of applications such as land cover classification, climate change studies and environmental monitoring; however, the acquisition of HSI is still costly, and the curse of dimensionality i.e. the phenomenon that the amount of required training samples increases exponentially as the dimensionality increases linearly, makes it difficult to exploit the full potential of machine learning in HSI classification. To resolve the problem, we propose a novel framework for spatial-spectral HSI classification in this article. A soft support vector machine (SVM) and a probabilistic joint sparsity model (JSM) are proposed to compute a posteriori probabilities of the test pixels, respectively; and the probability scores are then fused by a linear opinion pool. Furthermore, a Markov random field (MRF) model is used as a maximum a posteriori (MAP) segmentation method for further regularization of the neighbor information to derive the labels for pixels. Extensive experiments conducted on three commonly-used benchmarking data sets show that the proposed probabilistic fusion method outperforms a number of well-known spatial-spectral HSI classification techniques.