Polarisation information within a scene can be exploited in military systems to give enhanced automatic target detection and recognition (ATD/R) performance. However, the performance gain achieved is highly dependent ...
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ISBN:
(纸本)9781510609051;9781510609068
Polarisation information within a scene can be exploited in military systems to give enhanced automatic target detection and recognition (ATD/R) performance. However, the performance gain achieved is highly dependent on factors such as the geometry, viewing conditions, and the surface finish of the target. Such performance sensitivities are highly undesirable in many tactical military systems where operational conditions can vary significantly and rapidly during a mission. Within this paper, a range of processing architectures and fusion methods is considered in terms of their practical viability and operational robustness for systems requiring ATD/R. It is shown that polarisation information can give useful performance gains but, to retained system robustness, the introduction of polarimetric processing should be done in such a way as to not compromise other discriminatory scene information in the spectral and spatial domains. The analysis concludes that polarimetric data can be effectively integrated with conventional intensity-based ATD/R by either adapting the ATD/R processing function based on the scene polarisation or else by detection-level fusion. Both of these approaches avoid the introduction of processing bottlenecks and limit the impact of processing on system latency.
Simple yet robust techniques for detecting targets in infrared (IR) images are an important component of automatictargetrecognition (ATR) systems. In our previous works, we have developed IR targetdetection and tra...
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Simple yet robust techniques for detecting targets in infrared (IR) images are an important component of automatictargetrecognition (ATR) systems. In our previous works, we have developed IR targetdetection and tracking algorithms based on image correlation and intensity. In this paper, we discuss these algorithms, their performances and problems associated with them and then propose novel algorithms to alleviate these problems. Our proposed targetdetection and tracking algorithms are based on frequency domain correlation and Bayesian probabilistic techniques, respectively. The proposed algorithms are found to be suitable for real-time detection and tracking of static or moving targets, while accommodating for detrimental affects posed by the clutter and background noise. Finally, limitations of all these algorithms are discussed. (C) 2008 Elsevier Ltd. All rights reserved.
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