Dr. Andrew Niklas Profile

Dr. Andrew Niklas

at Etegent Technologies Ltd

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Proceedings Article | 1 August 2021 Presentation + Paper

An empirically-driven model for reflective-band polarization phenomenology

Bradley Ratliff, Andrew Niklas, Collin Bright

Proceedings Volume 11833, 118330R (2021) https://doi.org/10.1117/12.2595372

KEYWORDS: Sensors, Sun, 3D modeling, Polarimetry, Data modeling, Polarization, Reflectivity, Cameras, Electro optical modeling

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Polarization phenomenology is particularly complex at electro-optical and infrared wavelengths. The observed polarization state is dependent upon material surface characteristics, shape, chemistry, sensor geometry, relative position of any illumination sources, relative temperatures of scene and background objects, atmospheric conditions, and the presence and temperature of objects within the sensor field of view. First principles physics-based models often require full material characterization in the form of the polarimetric bidirectional reflectance distribution function (pBRDF). While pBRDF measurements can be reliably obtained for small target samples under controlled laboratory conditions, they are more challenging to obtain for many remote sensing targets of interest. Furthermore, in outdoor settings it is difficult to control pBRDF parameters such as atmospheric conditions, solar illumination position, and polarization state. The result is that pBRDF measurements are simply not available for many materials of interest at the level of fidelity required by physics-based models. Moreover, the level of accuracy capable with physics-based modeling tools is often not necessary for many tasks. For experiment or mission planning purposes, it is desirable to have a rough idea of expected polarization signatures for a given material class, time of day, and sensor look angle. Having simplified models that are capable of predicting expected polarimetric signatures, even at a low fidelity, is of high utility for many applications. Here we present the initial framework of such a model based upon empirical data measurements. Results are generated for several material classes with corresponding validation against physics-based models. We show that our measured Stokes vector and DoLP values are within expected physical bounds for 96% of the measured data and generally agree with truth results.

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