Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling

This paper focuses on the application of LID3 (linguistic decision tree induction algorithm) to the classification of weather radar images. In radar analysis a phenomenon known as bright band occurs. This essentially is an amplification in reflectivity due to melted snow and leads to overestimation of precipitation. It is therefore beneficial to detect this bright band region and apply the appropriate corrections. This paper uses LID3 in order to identify the bright band region pixel by pixel in real time. This is not possible with the current differencing methods currently used for bright band detection. LID3 also allows us to infer a set of linguistic rules to further our understanding of the relationship between radar measurements and the classification of bright band. A new idea called conditional labeling is proposed, which attempts to ensure a more efficiently partitioned space, omitting relatively sparse branches caused by attribute dependencies.