Abstract
A method is presented that allows information from ancillary data sources to be incorporated into the results of an existing classification of remotely sensed data. Based upon probabilistic label relaxation procedures, which are used for imbedding spatial context data in image-labeling problems, the method utilizes the source of ancillary information in the form of a set of probabilities. These are injected into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. Results are presented of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the manner outlined.
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