Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines

Abstract

Two-category support vector machines (SVMs) have become very popular in the machine learning community for classification problems and have recently been shown to have good optimality properties for classification purposes. Treating multicategory problems as a series of binary problems is common in the SVM paradigm. However, this approach may fail under a variety of circumstances. The multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case in a symmetric way, and has good theoretical properties, has recently been proposed. The proposed MSVM in addition provides a unifying framework when there are either equal or unequal misclassification costs, and when there is a possibly nonrepresentative training set. Illustrated herein is the potential of the MSVM as an efficient cloud detection and classification algorithm for use in Earth Observing System models, which require knowledge of whether or not a radiance profile is cloud free. If the profile is not cloud free, it is valuable to have information concerning the type of cloud, for example, ice or water. The MSVM has been applied to simulated MODIS channel data to classify the radiance profiles as coming from clear sky, water clouds, or ice clouds, and the results are promising. It can be seen in simple examples, and application to Moderate Resolution Imaging Spectroradiometer (MODIS) observations, that the method is an improvement over channel-by-channel partitioning. It is believed that the MSVM will be a very useful tool for classification problems in atmospheric sciences.