Managing the Spectral-Spatial Mix in Context Classification Using Markov Random Fields

A straightforward 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 can determined simply, guided by an assessment of the importance of the spatial contribution. Experimental results are presented using an artificial data set and real data recorded by the Landsat Thematic Mapper and Airborne Visible/Infrared Imaging Spectrometer.