Abstract A two-dimensional scene is analyzed statistically by superimposing on it a rectangular grid and studying each square picture element (pixel) as one unit of the entire picture. With each pixel we want to associate one of K labels, but the label must be determined. Labels may be related to one another spatially, reflecting an underlying pattern in the scene. There is a noisy observation vector associated with each pixel, and these vectors are used contextually to classify the pixels; that is, to determine their labels in an effort to reconstruct the true scene. The discretized picture and the K labels constitute a lattice structure. We assume there is prior information available to assist in the reconstruction. The adaptive Bayesian classification (ABC) procedure proposed is iterative. It starts with a formal predictive contextual Bayesian classification of the entire map, then proceeds by adaptively reclassifying all of the labels in the map at each iteration using an empirical Bayesian updating a...