Adaptive Bayesian contextual classification based on Markov random fields

In this paper an Adaptive Bayesian Contextual classification procedure that utilizes both spectral and spatial interpixel dependency contexts in estimation of statistics and classification is proposed. Essentially, this classifier is the constructive coupling of an adaptive classification procedure and a Bayesian contextual classification procedure. In this classifier, the joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Markov Random Field. The estimation of statistics and classification are performed in a recursive manner to allow the establishment of the positive feedback process in a computationally efficient manner. Experiments with real hyperspectral data show that, starting with a small training sample set, this classifier can reach classification accuracies similar to that obtained by a pixelwise MLC with a very large training sample set. Additionally, classification maps are produced which have significantly less speckle error.