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The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique—Hybrid Bayesian Classifier (HBC)—where the class prior probabilities are determined by unmixing a supplemental low spatial–high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial–low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments—first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS …