Automatic Segmentation and Classification of Infrared Meteorological Satellite Data

Abstract
Cloud-type classification is an important component of meteorological and hydrological programs which require estimates of parameters such as solar radiation, rainfall, moisture, and sea-surface temperature. The accuracy of automatic cloud-type classification systems has been limited by ambiguities in multispectral cloud type signatures. Attempts to resolve these ambiguities by the addition of textural features to brightness and temperature features have not produced a significant reduction in misclassification errors. The algorithm described presents a cloud-type classification system which resolves ambiguities in infrared cloud-type signatures by a comparison of textural measures on known and unknown cloud-type segments. The algorithm consists of two parts: 1) the segmentation procedure and 2) the classification procedure. The segmentation part of the algorithm provides a generalized method for partitioning a window of image data into objects characterized by nonoverlapping intervals of gray-level values. The classification part of the algorithm is problem specific, i.e., selection of window and segment features and decision rules will vary with the application. Application of the algorithm to the problem of classifying 107 windows of SMS-1 infrared satellite data resulted in a classification accuracy of approximately 95 percent.

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