Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland

We show how a series of satellite images used in conjunction with data derived from a digital terrain model can be used to monitor salinity in farmland. Using these data, a conditional probability network (CPN) is constructed to produce salinity maps. The maps are the result of combining uncertain information in images with uncertain knowledge or rules, where the rules are of a temporal and spatial nature. A specific model for extending conditional probability networks to handle the case of spatial context is given. To implement this model requires minor modifications to existing code for handling non-spatial CPN’s.