James K. Boger

The effects of thermal equilibrium and contrast in LWIR polarimetric images

Long-wave infrared (LWIR) polarimetric signatures provide the potential for day-night detection and identification of objects in remotely sensed imagery. The source of optical energy in the LWIR is usually due to thermal emission from the object in question, which makes the signature dependent primarily on the target and not on the external environment. In this paper we explore the impact of thermal equilibrium and the temperature of (unseen) background objects on LWIR polarimetric signatures. We demonstrate that an object can completely lose its polarization signature when it is in thermal equilibrium with its optical background, even if it has thermal contrast with the objects that appear behind it in the image.

Unpolarized calibration and nonuniformity correction for long-wave infrared microgrid imaging polarimeters

Recent developments for long-wave infrared (LWIR) imaging polarimeters include incorporating a microgrid polarizer array onto the focal plane array. Inherent advantages over other classes of polarimeters include rugged packaging, inherent alignment of the optomechanical system, and temporal synchronization that facilitates instantaneous acquisition of both thermal and polarimetric information. On the other hand, the pixel-to-pixel instantaneous field-of-view error that is inherent in the microgrid strategy leads to false polarization signatures. Because of this error, residual pixel-to-pixel variations in the gain-corrected responsivity, the noise-equivalent input, and variations in the pixel-to-pixel micropolarizer performance are extremely important. The degree of linear polarization is highly sensitive to these parameters and is consequently used as a metric to explore instrument sensitivities. We explore the unpolarized calibration issues associated with this class of LWIR polarimeters and discuss the resulting false polarization signature for thermally flat test scenes.

Image processing methods to compensate for IFOV errors in microgrid imaging polarimeters

Long-wave infrared imaging Stokes vector polarimeters are used in many remote sensing applications. Imaging polarimeters require that several measurements be made under optically different conditions in order to estimate the polarization signature at a given scene point. This multiple-measurement requirement introduces error in the signature estimates, and the errors differ depending upon the type of measurement scheme used. Here, we investigate a LWIR linear microgrid polarimeter. This type of instrument consists of a mosaic of micropolarizers at different orientations that are masked directly onto a focal plane array sensor. In this scheme, each polarization measurement is acquired spatially and hence each is made at a different point in the scene. This is a significant source of error, as it violates the requirement that each polarization measurement have the same instantaneous field-of-view (IFOV). In this paper, we first study the amount of error introduced by the IFOV handicap in microgrid instruments. We then proceed to investigate means for mitigating the effects of these errors to improve the quality of polarimetric imagery. In particular, we examine different interpolation schemes and gauge their performance. These studies are completed through the use of both real instrumental and modeled data.

Evaluation and display of polarimetric image data using long-wave cooled microgrid focal plane arrays

Recent developments for Long Wave InfraRed (LWIR) imaging polarimeters include incorporating a microgrid polarizer array onto the focal plane array (FPA). Inherent advantages over typical polarimeters include packaging and instantaneous acquisition of thermal and polarimetric information. This allows for real time video of thermal and polarimetric products. The microgrid approach has inherent polarization measurement error due to the spatial sampling of a non-uniform scene, residual pixel to pixel variations in the gain corrected responsivity and in the noise equivalent input (NEI), and variations in the pixel to pixel micro-polarizer performance. The Degree of Linear Polarization (DoLP) is highly sensitive to these parameters and is consequently used as a metric to explore instrument sensitivities. Image processing and fusion techniques are used to take advantage of the inherent thermal and polarimetric sensing capability of this FPA, providing additional scene information in real time. Optimal operating conditions are employed to improve FPA uniformity and sensitivity. Data from two DRS Infrared Technologies, L.P. (DRS) microgrid polarizer HgCdTe FPAs are presented. One FPA resides in a liquid nitrogen (LN2) pour filled dewar with a 80°K nominal operating temperature. The other FPA resides in a cryogenic (cryo) dewar with a 60° K nominal operating temperature.

Mitigation of image artifacts in LWIR microgrid polarimeter images

Microgrid polarimeters, also known as division of focal plane (DoFP) polarimeters, are composed of an integrated array of micropolarizing elements that immediately precedes the FPA. The result of the DoFP device is that neighboring pixels sense different polarization states. The measurements made at each pixel can be combined to estimate the Stokes vector at every reconstruction point in a scene. DoFP devices have the advantage that they are mechanically rugged and inherently optically aligned. However, they suffer from the severe disadvantage that the neighboring pixels that make up the Stokes vector estimates have different instantaneous fields of view (IFOV). This IFOV error leads to spatial differencing that causes false polarization signatures, especially in regions of the image where the scene changes rapidly in space. Furthermore, when the polarimeter is operating in the LWIR, the FPA has inherent response problems such as nonuniformity and dead pixels that make the false polarization problem that much worse. In this paper, we present methods that use spatial information from the scene to mitigate two of the biggest problems that confront DoFP devices. The first is a polarimetric dead pixel replacement (DPR) scheme, and the second is a reconstruction method that chooses the most appropriate polarimetric interpolation scheme for each particular pixel in the image based on the scene properties. We have found that these two methods can greatly improve both the visual appearance of polarization products as well as the accuracy of the polarization estimates, and can be implemented with minimal computational cost.

Measurement of the radiometric and polarization characteristics of a microgrid polarizer infrared focal plane array

Remote sensing applications make use of the optical polarization characteristics of a scene to enhance target detection and discrimination. Imaging polarimeters typically utilize polarizing arrays located in front of a focal plane array as a means of extracting polarization information from the optical scene. Over the last few years, technology development efforts have resulted in FPAs that integrate the polarizer with the infrared focal plane array (FPA). This paper will report on the radiometric and polarization characterization of a micro-grid polarizer FPA from DRS Infrared Technologies, L.P. (DRS). These measurements were performed to evaluate the radiometric performance and the polarization characteristics of the FPA.

Instrument simulation for estimating uncertainties in imaging polarimeters

Remote sensing of the polarized light emitted or reflected from a scene has the potential to improve object detection, object identification, or to enhance images. The need to accurately measure individual Stokes components depends on the application. In all applications, it is desirable to estimate the uncertainty associated with Stokes images. A process is described that uses an instrument computer simulation to compute expected uncertainties in imaging polarimeter data. The simulation takes measured and/or estimated uncertainties in individual instrument components and propagates them to evaluate their effect on the Stokes measurement. Individual uncertainties are then combined to obtain an overall uncertainty estimate for the given Stokes input. Methods of visualizing the effect of instrument uncertainty on various Stokes input states are also described.

Combatting infrared focal plane array nonuniformity noise in imaging polarimeters

One of the most significant challenges in performing infrared (IR) polarimetery is the focal plane array (FPA) nonuniformity (NU) noise that is inherent in virtually all IR photodetector technologies that operate in the midwave IR (MWIR) or long-wave IR (LWIR). NU noise results from pixel-to-pixel variations in the repsonsivity of the photodetectors. This problem is especially severy in the microengineered IR FPA materials like HgCdTe and InSb, as well as in uncooled IR microbolometer sensors. Such problems are largely absent from Si based visible spectrum FPAs. The pixel response is usually a variable nonlinear response function, and even when the response is linearized over some range of temperatures, the gain and offset of the resulting response is usually highly variable. NU noise is normally corrected by applying a linear calibration to the data, but the resulting imagery still retains residual nonuniformity due to the nonlinearity of the photodetector responses. This residual nonuniformity is particularly troublesome for polarimeters because of the addition and subtraction operations that must be performed on the images in order to construct the Stokes parameters or other polarization products. In this paper we explore the impact of NU noise on full stokes and linear-polarization-only IR polarimeters. We compare the performance of division of time, division of amplitude, and division of array polarimeters in the presence of both NU and temporal noise, and assess the ability of calibration-based NU correction schemes to clean up the data.

Modeling precision and accuracy of a LWIR microgrid array imaging polarimeter

Long-wave infrared (LWIR) imaging is a prominent and useful technique for remote sensing applications. Moreover, polarization imaging has been shown to provide additional information about the imaged scene. However, polarization estimation requires that multiple measurements be made of each observed scene point under optically different conditions. This challenging measurement strategy makes the polarization estimates prone to error. The sources of this error differ depending upon the type of measurement scheme used. In this paper, we examine one particular measurement scheme, namely, a simultaneous multiple-measurement imaging polarimeter (SIP) using a microgrid polarizer array. The imager is composed of a microgrid polarizer masking a LWIR HgCdTe focal plane array (operating at 8.3-9.3 μm), and is able to make simultaneous modulated scene measurements. In this paper we present an analytical model that is used to predict the performance of the system in order to help interpret real results. This model is radiometrically accurate and accounts for the temperature of the camera system optics, spatial nonuniformity and drift, optical resolution and other sources of noise. This model is then used in simulation to validate it against laboratory measurements. The precision and accuracy of the SIP instrument is then studied.

Error evaluation template for use with imaging spectro-polarimeters

Accurately identifying and bounding error sources in imaging spectro-polarimeters is a challenging task. Here we present an error evaluation methodology intended as an organizational tool for both itemizing and quantifying sources of error in polarimetric instruments. Associated with each source of error are both a metric and test by which these errors may be quantified. Using this procedure, we examine the accuracy and precision of a particular imaging Stokes vector hyper-spectral polarimeter. A subset of the identified error sources are selected and propagated through the system. These measured error quantities are then used to put absolute error bounds on the data acquired by our instrument. These measured error quantities are further documented and presented in the form of an error evaluation sheet.