An Automatic Ship Classification System for ISAR Imagery

II We have designed and developed an automatic ship classification (ASe) system for classifying potential naval targets from inverse synthetic-aperture radar (ISAR) imagery. Initially, the ASC system was developed for an over-thehorizon targeting application. In the application, an airborne platform with an on-board ISAR sensor transmitted imagery to a host ship carrying the ASC system. Our present focus is on placing the ASC system on board the sensor platform to assist the flight crew in classifying naval vessels. The current ASC system uses both neural network and conventional processing techniques to determine the ship class of a target from ISAR imagery acqnired during reconnaissance missions. An Adaptive Clustering Network (ACN) allows a single ship class to be distributed across several categories so that the system develops a degree of invariance to target motion. We have evaluated the ASC system on a limited set of actual ISAR imagery collected during operator training missions and on a larger database of imagery from IBM. Our preliminary results indicate that an operational ASC system with performance levels comparable to human operators can be achieved. From these results, we feel that the ASC system is now ready for a thorough field evaluation on board an ISAR sensor platform. ./