A machine vision based method for atmospheric circulation classification

Weather maps refer to meteorological data that characterize the atmospheric circulation in a region. The classification of weather maps into categories becomes an important task for understanding regional climate. Towards this goal, manual and semiautomatic techniques have been used, requiring manpower and supervision. In this paper, we propose a machine vision based method for the classification of weather maps into distinct classes. The chain code descriptor is applied to extract the feature of isobaric lines and we introduce the double-side chain code (DSCC) histogram for feature representation. Handling DSCC histograms as multidimensional vectors, the k-nearest neighbors (k-NN) algorithm classifies the objects to an appropriate number of classes, based on closest training set in the feature space. This method provides an automated and more dasiaobjectivepsila classification scheme, applying straightforward to the input weather map´s image.