Robust fusion-based processing for military polarimetric imaging systems

Duncan L. Hickman; Moira I. Smith; Kyung Su Kim; Hyun-Jin Choi

doi:10.1117/12.2261689

1 May 2017 Robust fusion-based processing for military polarimetric imaging systems

Duncan L. Hickman; Moira I. Smith; Kyung Su Kim; Hyun-Jin Choi

Author Affiliations +

Proceedings Volume 10202, Automatic Target Recognition XXVII; 1020202 (2017) https://doi.org/10.1117/12.2261689
Event: SPIE Defense + Security, 2017, Anaheim, California, United States

Abstract

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.

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Duncan L. Hickman, Duncan L. Hickman, Moira I. Smith, Moira I. Smith, Kyung Su Kim, Kyung Su Kim, Hyun-Jin Choi, Hyun-Jin Choi, } "Robust fusion-based processing for military polarimetric imaging systems", Proc. SPIE 10202, Automatic Target Recognition XXVII, 1020202 (1 May 2017); doi: 10.1117/12.2261689; https://doi.org/10.1117/12.2261689

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