VEG has codified this expert knowledge into a rule-based decision component for determining which technique to use. VEG provides a comprehensive interface that makes applying the techniques simple and aids a researcher in developing and testing new techniques. VEG also allows the scientist to incorporate historical databases into problem solving. The scientist can match the target data being studied with historical data so the historical data can be used to provide the coefficients needed for applying analysis techniques. The historical data also provides more accurate error estimates than were previously available.
VEG also provides a classification algorithm that can learn new classes of surface features.
The learning system uses the database of historical cover types to learn class descriptions of one or more classes of cover types. These classes can include broad classes such as soil or vegetation or more specific classes such as forest, grass, and wheat. The learning system uses sets of positive and negative examples from the historical databases to find the most important features that uniquely distinguish each class. The system then uses the learned classes to classify an unknown sample by finding the class that best matches the unknown cover type data. The learning systems also include an option that allows the user to test the system's classification performance.