<title>Hierarchical classifier design for airborne SAR images of ships</title>

Abstract

We report about a hierarchical design for extracting ship features and recognizing ships from SAR images, and which will eventually feed a multisensor data fusion system for airborne surveillance. The target is segmented from the image background using directional thresholding and region merging processes. Ship end-points are then identified through a ship centerline detection performed with a Hough transform. A ship length estimate is calculated assuming that the ship heading and/or the cross-range resolution are known. A high-level ship classification identifies whether the target belongs to Line (mainly combatant military ships) or Merchant ship categories. Category discrimination is based on the radar scatterers' distribution in 9 ship sections along the ship's range profile. A 3-layer neural network has been trained on simulated scatterers distributions and supervised by a rule- based expert system to perform this task. The NN 'smoothes out' the rules and the confidence levels on the category declaration. Line ship type (Frigate, Destroyer, Cruiser, Battleship, Aircraft Carrier) is then estimated using a Bayes classifier based on the ship length. Classifier performances using simulated images are presented.