This PDF file contains the front matter associated with SPIE Proceedings Volume 11412, including the Title Page, Copyright Information, and Table of Contents.

Division of focal plane (DoFP), or integrated microgrid imaging polarimeters, typically consist of a 2x2 mosaic of linear polarization filters overlaid upon a focal plane array sensor and obtain temporally synchronized polarized intensity measurements across a scene, similar in concept to a Bayer color filter array camera. However, the resulting estimated polarimetric images suffer a loss in resolution and can be plagued by aliasing due to the modulated microgrid measurement strategy. Demosaicing strategies have been proposed that attempt to minimize these effects, but result in some level of residual artifacts. In this work, we present a conditional and guided generative adversarial network (GAN) strategy for demosaicing integrated microgrid polarimeter imagery. The GAN is trained using high resolution polarized intensity measurements that contain minimal spatial aliasing artifacts obtained from a division-of-time polarimeter. We apply the algorithm to test data collected from real visible microgrid imagery and compare the results with other state-of-the-art microgrid demosaicing strategies.

Neural networks trained on RGB and monochromatic images are tested on images augmented by polarimetry for recognition of road-based objects. The goal of this work is to understand the scene conditions for which object detection and recognition can be improved by linear Stokes measurements. Shadows, windows, low albedo, and other object features which reduce RGB image contrast also decrease neural network detection performance. This work demonstrates specific cases for which linear Stokes images increase image contrast and therefore increase object detection by a neural network. Linear Stokes videos for five difference scenes are collected at three times of day and two driving directions. Although limited in scope, this work demonstrates some enhancement to object detection by adding polarimetry to neural networks trained on RGB images.

The Mueller matrix dependence on an objects' albedo and surface texture are measured and these effects are used to inform modifications to a microfacet model for polarized light scattering. Four different textures are imparted on red LEGO bricks which are illuminated at 451 nm and 662 nm. These wavelengths yield measurements for both low and high albedo conditions respectively. Analysis of polarizance, depolarization index, polarization entropy, and matrix roots demonstrate that texture and albedo have distinct polarization and depolarization signatures. The root mean square deviations (RMSD) of the unmodified microfacet model from the measurements are about three times greater for high albedo compared to low albedo measurements. The surface texture trend is more subtle, but in all cases the RMSD decreases as roughness increases. A major contribution of this work is an adjustment to the microfacet model so that the polarized term is wavelength dependent. This adjusted model improves the RMSD more for the low albedo compared to the high albedo measurements. To improve model fits to high albedo measurements, a modified depolarization structure is introduced to reduce the RMSD of high albedo measurements by about a factor of two.

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.

Long-Wave Infrared (LWIR) polarimetric measurements can be used to characterize space objects under certain conditions. Both visible and LWIR polarimetry have been demonstrated extensively in terrestrial applications for detection and characterization of objects of interest. Visible polarimetry has also been demonstrated for space object detection. The objective of the current research is to use a software model to determine how well an object can be detected in low Earth orbit (LEO) with LWIR polarimetry using a modest aperture, diffraction-limited telescope (70cm aperture), and whether it can be differentiated from another object of different composition. Most targets at this range and wavelength are effectively point sources with an aggregate value for their degree of linear polarization, somewhat dependent on target rotation with respect to the sensor. This approach represents a step forward in optical systems for space situational awareness in that it can be used both day and night, regardless of external target illumination.

Spectral imaging is heavily deployed in plant phenotyping due to its ability to infer aspects of the metabolome; however, observations of individual leaves are confounded by bidirectional reflectance effects. In this paper, we rigorously characterize these effects through the setup and implementation of a polarized Bidirectional Reflectance Distribution Function (pBRDF) Mueller matrix spectropolarimeter. We detail calibration procedures needed to correct for geometric and polarimetric aberrations, and demonstrate the pBRDF measurements for a maize (Zea mays) leaf. Furthermore, we present fixed transmission and reflection-mode measurements to monitor diurnal spectral Mueller-matrix dependencies caused by photosynthesis.

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.

When a target is embedded in random media, the quality of optical imaging can be improved by actively controlling the illumination and by exploiting vector wave properties. A rigorous description, however, requires expensive computational resources to fully account for the electromagnetic boundary conditions. Here we introduce a statistically-equivalent, scaling model that allows reducing significantly the complexity of the problem. The new scheme describes the entanglement between the local wave vector and the polarization state in random media, and also accounts for cumulative properties such as geometric phase. The approach is validated for different scenarios where the coherent background noise alters substantially the performance of active imaging.

Recent technology advancements in imaging technology has led to the commercialization of the first color-polarization imaging sensor. This technological feat is enabling development of new imaging applications and algorithms, which were not possible without this technology. However, when we compare several attributes between state-of-the-art color and polarization imaging technology, several shortcomings are evident in the polarization technology. First, the pixel pitch of today’s color technology is around 0.8 microns. The most advanced polarization imager utilized 3.5-micron pixel pitch – color imaging technology achieved this pixel pitch more than 10 years ago. However, today’s color imaging technology is plagued with optical and electrical cross talk. Although signal processing algorithms mitigate some of these effects, color technology is less stringent on the efficacy of these algorithms. Polarization imaging technology is fundamentally different from color and is more dependent on crosstalk. In this paper, we present theoretical data on how cross talk affects polarization accuracy.

Spectropolarimetry, the simultaneous measurement of spectrum and polarization, provides a wealth of information on environmental processes, particularly aerosols and water-borne particles. The SPEX method of measuring polarization through spectral modulation is used to accurately measure aerosol optical depth on ground-based, air-based, and space-based systems. This incudes SPEXone, a spectropolarimeter on NASA's new climate satellite PACE, as well as iSPEX, a smartphone-based single-beam implementation originally designed in 2012. iSPEX was previously used in citizen science projects with thousands of volunteers to measure aerosol optical depth across the Netherlands and Europe. However, it was limited in accuracy by the smartphone camera and the single-beam SPEX implementation. Furthermore, it only supported a few smartphone models. We present iSPEX 2, a completely new design with universal smartphone support as well as a dual-beam design for full spectroscopy and linear polarimetry. Using the SPECTACLE method for spectral and radiometric calibration of consumer cameras, any smartphone can provide quantitative spectral and polarimetric data. Due to its low cost, iSPEX 2 is well-suited to use in underprivileged areas, large-scale deployment, and citizen science projects.

The trade-off between spectral resolution and instrument throughput is analyzed for a compact, uncooled, longwave infrared (LWIR) channeled spectropolarimeter (IRCSP). The IRCSP was developed as a part of the Sub-mm Wave and InfraRed Polarimeters (SWIRP) project out of NASA's Goddard Spaceflight Center. The IRCSP scientific objective targets measurements of AOLP and DOLP with 1-µm spectral resolution from 8.5 - 12.5µm in a single snapshot. The geometry of the field stop determines the field of view (FOV) of the IRCSP. This work relates the spectral resolution, instrument throughput, and polarimetric accuracy of a spectro-polarimeter to the FOV. The accuracy of linear Stokes retrievals for low temperature thermal targets are predicted for varying FOV and measurement noise conditions. This work presents a method to quantify the achievable accuracy in AOLP and DOLP as a function of field stop dimensions and signal-to-noise ratio (SNR). While smaller field stops are shown to improve accuracy as the spectral resolution is increased, low SNR is the dominant source of error for the IRCSP prototype. For the IRCSP, a SNR of at least 80 is required to produce DOLP measurements with < 5% error for targets with DOLP < 0.2.

We demonstrate a single pulse LiDAR polarimeter that is optimized to measure the diagonal elements of a target Mueller matrix and thereby dramatically reduce CSWAP of the system. Previous work showed that 12 out 16 elements of a Mueller Matrix can be resolved using three polarization state analyzers (PSA); for example, a linear horizontal/vertical PSA, a linear +45º/135º PSA, and a circular PSA. Here we employ a single elliptical PSA to measure the diagonal matrix elements. The system is composed of a transmitter beam produced by directing the laser pulse through a Pockels cell wherein a high-voltage ramp is synchronously applied thereby creating a time varying birefringence. The resulting pulse is characterized by a time varying polarization across the temporal envelope. The receiver PSA is composed of an elliptical PSA (quarter wave plate at angle Ɵ and linear polarizer) followed by a high bandwidth detector capable of measuring the polarization modulation of the return signal. In this particular work, we use analytical models to optimize the Pockels cell angle and the elliptical PSA quarter-wave plate angle for maximum matrix element estimation accuracy. A system demonstrator employing a 1.06 μm, 10 ns pulse laser is used to demonstrate target diagonal Mueller matrix measurement. We measure diagonal Mueller matrix elements of air, Spectralon, and paint samples.

As the applications of hyperspectral imaging rapidly diversify, the need for accurate radiometric calibration of these imaging systems is becoming increasingly important. When performing radiometric measurements, the polarization response of the imaging system can be of particular interest if the scene contains partially polarized objects. For example, when imaging a scene containing water, surface reflections from the water will be partially polarized, possibly affecting the response of the imaging system. In this paper, the polarization response of a Resonon, Inc. visible near-infrared (VNIR) hyperspectral imaging system is assessed across a spectral range of 400nm to 1000 nm, with a spectral resolution of 2.1 nm. Efforts are currently underway to correct for the observed polarization response of the imaging system.

Although polarization is historically defined as a local property, one can also regard it as a nonlocal feature of correlated fields for which the correlation between the field components at any locations and fluctuating along any orthogonal directions does not factorize. For such fields, the local states of polarization are randomly distributed on the surface of the Poincare sphere while their average lie somewhere inside of it, but not necessarily at its center. An average degree of polarization can then be defined as distance between this point and the origin of the sphere. Of course, because of nonlocality, there will be different types of randomly polarized fields which, on average, will have the same average degree of polarization. Because these fields could have different physical origins or can be the result of different field transformations, finding a proper way to discriminate among them is an important issue. We introduce the scalar average similarity of an ensemble of randomly polarized states. This global measure is based on the complex degree of mutual polarization between any pair of vector fields in the ensemble. We show that, in the case of fully-correlated and globally unpolarized fields, the variation of this parameter is bounded and its value can effectively discriminate between different configurations of pure states.