Dr. Bryan L. Holtsberry Profile

Dr. Bryan L. Holtsberry

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Proceedings Article | 21 April 2020 Paper

Experiments in multiple-waveband passive polarimetric and active infrared imaging for material classification

Jarrod Brown, Bryan Holtsberry, Darrell Card, Daniel Short

Proceedings Volume 11412, 1141209 (2020) https://doi.org/10.1117/12.2560286

KEYWORDS: Polarimetry, LIDAR, Long wavelength infrared, Sensors, Mid-IR, Polarization, Reflectivity, Imaging systems, Calibration, Infrared imaging

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Unique laboratory experiments are conducted using multiple waveband passive polarimetric and active infrared imaging systems to measure the optical signature of a diverse sample set in support of innovative research in material classification. The primary objective of this work is to explore the feasibility of utilizing multiple sensors of varying waveband or modality to enable or improve classification of common materials relevant in remote sensing applications. This objective includes current remote sensing technologies such as passive polarimetric imaging across multiple infrared wavebands, and light detection and ranging (LiDAR) active imaging. Therefore, to fully explore this objective, representative measurements of diverse materials are collected with three passive polarimeters and a LiDAR system. The measurements characterize material properties such as bidirectional reflectivity, directional emissivity, and surface roughness, which can be used for material classification. Typical passive polarimetric classification techniques assume the polarized signature is generated by reflection, and the imaging geometry is known. We propose to utilize both the polarized signature created by reflections as well as self-emission from the material. The reflectivity and imaging geometry estimations are assisted with the inclusion of LiDAR measurements. We present details of the experiment setup, sample set, analysis of imagery, and observations drawn from experimental results. The capability of classifying materials using passive polarimetric and active infrared imaging systems is investigated.

Proceedings Article | 21 April 2020 Paper

Updated material identification from remote sensing of polarized self-emission

Bryan Holtsberry, David Voelz

Proceedings Volume 11412, 1141205 (2020) https://doi.org/10.1117/12.2560273

KEYWORDS: Polarimetry, Mid-IR, Metals, Long wavelength infrared, Dielectrics, Reflectivity, Dielectric polarization, Remote sensing, Refractive index, Diffuse reflectance spectroscopy

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An important application for remote sensing is the detection and discrimination of targets of interest. The detection and discrimination problem is not trivial, especially if the target blends with its background or when decoys are deployed. Remote sensing systems can utilize imaging polarimetry to identify the materials from which targets are made. A fundamental property of a material is its complex index of refraction, which can be calculated from the material’s degree of linear polarization (DoLP). Previously, we reported on a technique for estimation of the complex index of refraction (CIR) using measurements of the polarized radiance from a material’s self-emission. The materials were measured with an imaging polarimeter that operates in the mid-wave infrared spectral region. Several improvements to our processes have been implemented since the earlier work and these improved processes have led to improved results, which are detailed in this paper. A larger set of materials was measured and analyzed, including measurements with a new imaging polarimeter, which operates in the long-wave infrared spectral region. We also made improvements to our model for the degree of linear polarization of a material. This model is used in conjunction with the DoLP calculated from the measured data to estimate the CIR, which is a fundamental property of materials and can therefore be used to identify a material. An initial goal of this work is to use the technique to discriminate between metals and dielectrics. We demonstrate the ability to discriminate between metals and dielectrics with the estimated CIR results. There is a clear difference for the estimated index of refraction values, and an even more significant difference for the coefficient of extinction values, obtained for metals versus the values obtained for dielectrics. These differences in estimated values provide a means of discriminating metals from dielectrics.

Proceedings Article | 6 September 2019 Presentation + Paper

Material identification from remote sensing of polarized self-emission

Bryan Holtsberry, David Voelz

Proceedings Volume 11132, 1113203 (2019) https://doi.org/10.1117/12.2528282

KEYWORDS: Polarimetry, Aluminum, Data modeling, Remote sensing, Mid-IR, Mica, Refraction, Refractive index, Long wavelength infrared, Imaging systems

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An important application for remote sensing is the detection and discrimination of targets of interest. Polarimetry can be used by remote sensing systems to identify the materials from which targets are made. If an imaging polarimeter is used, the target can also be resolved spatially. A fundamental material property is its complex index of refraction, which can be calculated from polarimetric measurements of the material. Previous work has shown the feasibility of estimating a material’s complex index of refraction from measurements of the polarized reflected radiance in the visible and nearinfrared spectral regions. A new technique is being developed for estimation of the complex index of refraction using measurements of the polarized radiance from a material’s self-emission. Measurements are made in the mid-wave infrared and spectral regions and are used to calculate the Stokes vector, which is then used to calculate the degree of linear polarization (DoLP). An equation is derived for the DoLP as a function of the Fresnel coefficients, which are themselves a function of the complex index of refraction and the angle of emission. Complex index of refraction values calculated from measured material DoLP values are presented. An initial goal of this work is to use the technique to discriminate between metals and dielectrics.

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