6 April 1995 Automatic ship classification system for inverse synthetic aperture radar (ISAR) imagery
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Abstract
The U.S. Navy has been interested in applying neural network processing architectures to automatically determine the naval class of ships from an inverse synthetic aperture radar (ISAR) on-board an airborne surveillance platform. Currently an operator identifies the target based on an ISAR display. The emergence of the littoral warfare scenario, coupled with the addition of multiple sensors on the platform, threatens to impair the ability of the operator to identify and track targets in a timely manner. Thus, on-board automation is quickly becoming a necessity. Over the past four years the Opto-Radar System Group at MIT Lincoln Laboratory has developed and fielded a neural network based automatic ship classification (ASC) system for ISAR imagery. This system utilizes imagery from the APS-137 ISAR. Previous related work with ASC systems processed either simulated or real ISAR imagery under highly controlled conditions. The focus of this work was to develop a ship classification system capability of providing real-time identification from imagery acquired during an actual mission. The ship classification system described in this report uses both neural network and conventional processing techniques to determine the naval class of a ship from a range- Doppler (ISAR) image. The `learning' capability of the neural network classifier allows a single naval class to be distributed across many categories such that a degree of invariance to ship motion is developed. The ASC system was evaluated on 30 ship class database that had also been used for an operational readiness evaluation of ISAR crews. The results of the evaluation indicate that the ASC system has a performance level comparable to ISAR operators and typically provides a significant improvement in throughput.
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Murali M. Menon, Murali M. Menon, } "Automatic ship classification system for inverse synthetic aperture radar (ISAR) imagery", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205142; https://doi.org/10.1117/12.205142
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