<title>Support vector machines and target classification</title>

Abstract

The area of automatic target classification has been a difficult problem for many years. Many approaches involve extracting information from imagery through a variety of statistical filtering and sampling techniques, resulting in a reduced dimension feature vector that is the input for a learning algorithm. The Support Vector Machine (SVM) algorithm is a wide margin classifier that provides reasonable results for sparse data sets. This can allow one to avoid the feature extraction step and process images directly. The SVM algorithm has the additional benefits that there are few parameters to adjust and the solutions are unique for a given training set. We applied SVM to a variety of data sets, including character recognition, military vehicles and Synthetic Aperture Radar data, and compared the results to some standard neural network architectures. It was found that the SVM algorithm gave equivalent or higher correct classification results compared to the neural networks with some noted advantages.