Polarimetric sensing provides additional scene information which can be used to enhance the target detection and recognition performance of an imaging system. Such information is particularly valuable in the discrimination of weak target signatures from their surroundings and, as such, is attracting a growing interest for future military and surveillance applications. However, the extraction of polarisation information adds complexity in terms of the physical sensor design and the required data processing. Imaging polarimeters typically require four images to be captured of the same scene in different polarisation orientations through either time or spatial division techniques. These sensor architectures introduce system performance constraints in terms of temporal and spatial resolution as well as the attendant degradations associated with the use of additional optical components. Issues associated with the physical envelope, operational robustness, and cost must also be considered. In terms of the processing of the polarimetric data, accurate registration and calibration is required to extract small polarisation signatures which are typically found in natural scenes. The polarimetric image data must then be processed using an image fusion or data fusion method which introduces further demands on the software design and system processor. For some applications, these limitations are acceptable relative to the polarimetric gain, whilst in others a conventional imaging sensor may offer a better overall solution. Consequently, it is important that a trade-off analysis is undertaken which evaluates the realistic performance gains with respect to the implementation and cost issues.
An ongoing challenge for many military imaging systems is the detection and classification of weak target signatures in a cluttered environment. In such cases, the use of image contrast and relative target motion alone does not always provide a sufficient level of target discrimination to give operational confidence and it is therefore necessary to consider the use of other discriminatory scene information. Polarisation is one such source of information and this paper reports on an extensive series of polarimetric trials undertaken across the visible, NIR, SWIR, MWIR and LWIR spectral bands. Using this data, the benefits and limitations of polarisation discrimination are reviewed in the context of practical military scenarios. It is shown that polarisation signatures vary with viewing geometry and atmospheric conditions. This would lead to an unpredictable performance level if the sensor discrimination was based solely on polarisation. However, by carefully combining polarisation with other scene information, useful operational benefits can be obtained and this is illustrated through a consideration of different data fusion approaches.
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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