An interactive hybrid expert system for polar cloud and surface classification

An interactive hybrid expert system is developed to classify polar scenes using AVHRR LAC imagery. A total of 183 spectral and textural signatures are generated from which the 20 “best” are chosen using the Sequential Forward Selection procedure. These 20 features are used to populate the working memory of the expert system. A probabilistic neural network is used as the inference engine to make probabilistic estimates of class membership. As part of the inference engine, a sophisticated outlier test is performed to provide a measure of classification confidence. During a session, the user is provided with an extensive set of on‐screen aids to assist in labelling. The user may modify the knowledge base by adding new samples to existing classes or by including new classes. The expert system provides confidence measures and a distance measure from the proposed class cluster centre. The interactive environment allows the user to test the impact of class labelling upon the knowledge base before new data is entered. For users working with very large datasets and very complex scenes, the integrity of the knowledge base is the primary concern. A bootstrap method is used to validate classification accuracy. On the basis of 100 bootstrap samples, an overall classification accuracy of 87% is achieved, with a standard deviation of 1%. The result is that much more accurate cloud classification in polar regions now can be made, which will aid us in our monitoring of global climate changes.