Utilizing feedback in adaptive SAR ATR systems

Owen Horsfield; David Blacknell

doi:10.1117/12.818437

28 April 2009 Utilizing feedback in adaptive SAR ATR systems

Owen Horsfield, David Blacknell

Proceedings Volume 7337, Algorithms for Synthetic Aperture Radar Imagery XVI; 73370M (2009) https://doi.org/10.1117/12.818437
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

Citation Download Citation

Owen Horsfield and David Blacknell "Utilizing feedback in adaptive SAR ATR systems", Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370M (28 April 2009); https://doi.org/10.1117/12.818437

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KEYWORDS

Synthetic aperture radar

Automatic target recognition

Databases

Target detection

Classification systems

Distance measurement

Computer aided design

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