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Controlling the spectral-spatial mix in context classification using Markov Random Fields

A simple method is presented for determining the most appropriate weighting of the spectral and spatial contributions in the Markov random field approach to context classification. The spectral and spatial components are each normalized to fall in the range (0,1) after which the appropriate value for the weighting coefficient is determined. A simple, systematic evaluation of the impact of the choice of weight on the ultimate classification results achieved is possible as a methodology for optimum weight selection. Experimental results are presented using an artificial data set and real data recorded by the TM and AVIRIS sensors.