Contextual data fusion applied to forest map revision

The use of a Markov random field model for multisource classification for map revision applications is investigated. A statistical model is presented, in which data from several remote sensing sensors is merged with spatial contextual information and a previous labeling of the scene from an existing thematic map to reach a consensus classification. The method is tested on two data sets for forest classification, and the classification performance is studied in terms of the effect of using remote sensing data from different sensors, the effect of spatial context, and the effect of using map data from previous surveys in the classification. It is shown that the use of a contextual classifier or an existing map of the area can have larger influence on the classification accuracy than using data from an additional sensor.