Classification of SAR Ship Images with the Aid of a Syntactic Pattern Recognition Algorithm

Synthetic aperture radar systems have made possible the generation of radar images of ships with high enough resolution to allow numerous targets or scatterers to be visible. With the availability of numerous scatterers in one radar image, it is theoretically possible to identify the class of a ship. The SAR image of a ship is a function of location of scatterers, SAR system frequency, radar-to-ship viewing angle, amount and type of sea-induced ship motion, and length of aperture. Because of the dependence on these variables, the number of images representing any one ship is large. It is the job of radar operator to study and understand the many radar images that can be encountered, and attempt to make the correct classification. Fast classification response times are required, since these images would normally be acquired in real time. A syntactic pattern recognition algorithm called the Coarse Feature Classifier CFC has been developed to aid the radar operator to perform the task of classifying SAR images of ships. By having the algorithm perform some of the tasks that the operator normally performs, one obtains the potential benefits of improved accuracy and speed of classification, and reduced operator fatigue. The algorithm extracts numerous features from the input SAR image which are then compared to a library of similar features in order to select the ships from the library which best resembles the input ship image. Details of the operation of the CFC are discussed.