Modeling and Simulation of a Long-Wave Infrared Polarimetric Sensor for Space Object Detection and Characterization

Long-Wave Infrared (LWIR, wavelength \u3e 8 um) polarimetric measurements can be used to characterize space objects. A simulation of a sensor for collection of LWIR polarimetric signatures of space objects has been assembled using two software packages: MATLAB, and FRED. A statistical approach developed for unresolved visible light polarimetric observations of GEO satellites has been adapted for unresolved LWIR polarimetric observations of LEO satellites, showing both that well-known objects can be recognized and anomalies--for example, a major change in shape due to the presence in the scene of another object--can be detected. Though the satellites are effectively point sources, the aggregate polarization values across many measurements can be used to differentiate objects of different shape and material composition

Polarimetric modeling of remotely sensed scenes in the thermal infrared

This dissertation develops a polarimetric thermal infrared (IR) framework within the Digital Image and Remote Sensing Image Generation (DIRSIG) software tool enabling users in the remote sensing community to conduct system level trades and phenomenology studies. To support polarized reflection and emission modeling within DIRSIG, a generalized bi-directional reflectance distribution function (BRDF) is presented. This generalized form is a 4x4 element Mueller matrix that may be configured to resemble the commonly utilized Beard-Maxwell or Priest-Germer BRDF models. A polarized emissivity model is derived that leverages a hemispherical integration of the polarized BRDF and Kirchoff\u27s Law. A portable experimental technique for measuring polarized long-wave IR emissivity is described. Experimental results for sixteen target and background materials are fit to the polarized emissivity model. The resulting model fit parameters are ingested by DIRSIG to simulate polarized long-wave infrared scene phenomenology. Thermally emitted radiance typically has a vertical polarization orientation, while reflected background radiance is polarized horizontally. The balance between these components dictates what polarized signature (if any) is detected for a given target. In general, specular targets have a stronger emission polarization signature compared to diffusely scattering targets consistent with visible polarimetry findings. However, the influence of reflected background radiance can reduce the polarimetric signature of specular targets below a detectable threshold. In these situations, a diffusely scattering target may actually exhibit a polarization signature stronger than a specular target material. This interesting phenomenology is confirmed by experimental scene collections and DIRSIG simulations. Understanding polarimetric IR phenomenology with this level of detail is not only key for system design, but also for determining optimal collection geometries for specific tactical missions

Hyperspectral and Hypertemporal Longwave Infrared Data Characterization

The Army Research Lab conducted a persistent imaging experiment called the Spectral and Polarimetric Imagery Collection Experiment (SPICE) in 2012 and 2013 which focused on collecting and exploiting long wave infrared hyperspectral and polarimetric imagery. A part of this dataset was made for public release for research and development purposes. This thesis investigated the hyperspectral portion of this released dataset through data characterization and scene characterization of man-made and natural objects. First, the data were contrasted with MODerate resolution atmospheric TRANsmission (MODTRAN) results and found to be comparable. Instrument noise was characterized using an in-scene black panel, and was found to be comparable with the sensor manufacturer\u27s specication. The temporal and spatial variation of certain objects in the scene were characterized. Temporal target detection was conducted on man-made objects in the scene using three target detection algorithms: spectral angle mapper (SAM), spectral matched lter (SMF) and adaptive coherence/cosine estimator (ACE). SMF produced the best results for detecting the targets when the training and testing data originated from different time periods, with a time index percentage result of 52.9%. Unsupervised and supervised classication were conducted using spectral and temporal target signatures. Temporal target signatures produced better visual classication than spectral target signature for unsupervised classication. Supervised classication yielded better results using the spectral target signatures, with a highest weighted accuracy of 99% for 7-class reference image. Four emissivity retrieval algorithms were applied on this dataset. However, the retrieved emissivities from all four methods did not represent true material emissivity and could not be used for analysis. This spectrally and temporally rich dataset enabled to conduct analysis that was not possible with other data collections. Regarding future work, applying noise-reduction techniques before applying temperature-emissivity retrieval algorithms may produce more realistic emissivity values, which could be used for target detection and material identification

Polarimetric Calibration and Characterization of the Telops Field Portable Polarimetric-Hyperspectral Imager

The Telops polarimetric-hyperspectral imager combines polarimetric and hyperspectral technologies to enable enhanced scene characterization. The Defense Threat Reduction Agency funded research at AFIT to leverage this capability to provide more accurate scene information to radiation transport models that will allow for more effective location of radiation sources within a region of interest. To support the objectives of the DTRA effort, there is a requirement for highly accurate radiometric, polarimetric, and spectral data on a pixel-by-pixel basis. The complex nature of the Telops instrument combined with working in the thermal IR waveband makes achieving this accuracy a challenge. This thesis develops a calibration methodology that enables high data accuracy in all three domains. In the process, a mathematical calibration framework was developed that links standard Fourier transform spectrometer (FTS) calibration with standard polarimetric calibration in a straightforward manner. This provided a framework for understanding the influence of various instrument parameters (both ideal and non-ideal) on ultimate calibration performance. The framework developed is utilized to quantify the non-idealities of the system and to characterize the performance of the spectro-polarimetric calibration. Additionally, fundamental performance limits are characterized including the noise equivalent spectral radiance and noise equivalent degree of linear polarization of the system

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

Multidimensional Optical Sensing and Imaging Systems (MOSIS): From Macro to Micro Scales

Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3-D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3-D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3-D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral-imaging-based multidimensional optical sensing and imaging systems (MOSIS), which can be used for 3-D visualization, seeing through obscurations, material inspection, and object recognition from microscales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2-D) images or conventional 3-D images. In addition, we present recent progress and applications of 3-D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave-infrared photon counting, 3-D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid-crystal devices, and 3-D endoscopy for healthcare applications.B. Javidi wishes to acknowledge support by the National Science Foundation (NSF) under Grant NSF/IIS-1422179, and DARPA and US Army under contract number W911NF-13-1-0485. The work of P. Latorre Carmona, A. Martínez-Uso, J. M. Sotoca and F. Pla was supported by the Spanish Ministry of Economy under the project ESP2013-48458-C4-3-P, and by MICINN under the project MTM2013-48371-C2-2-PDGI, by Generalitat Valenciana under the project PROMETEO-II/2014/062, and by Universitat Jaume I through project P11B2014-09. The work of M. Martínez-Corral and G. Saavedra was supported by the Spanish Ministry of Economy and Competitiveness under the grant DPI2015-66458-C2-1R, and by the Generalitat Valenciana, Spain under the project PROMETEOII/2014/072

Exploitation of infrared polarimetric imagery for passive remote sensing applications

Polarimetric infrared imagery has emerged over the past few decades as a candidate technology to detect manmade objects by taking advantage of the fact that smooth materials emit strong polarized electromagnetic waves, which can be remotely sensed by a specialized camera using a rotating polarizer in front of the focal plate array in order to generate the so-called Stokes parameters: S0, S1, S2, and DoLP. Current research in this area has shown the ability of using such variations of these parameters to detect smooth manmade structures in low contrast contrast scenarios. This dissertation proposes and evaluates novel anomaly detection methods for long-wave infrared polarimetric imagery exploitation suited for surveillance applications requiring automatic target detection capability. The targets considered are manmade structures in natural clutter backgrounds under unknown illumination and atmospheric effects. A method based on mathematical morphology is proposed with the intent to enhance the polarimetric Stokes features of manmade structures found in the scene while minimizing its effects on natural clutter. The method suggests that morphology-based algorithms are capable of enhancing the contrast between manmade objects and natural clutter backgrounds, thus, improving the probability of correct detection of manmade objects in the scene. The second method departs from common practices in the polarimetric research community (i.e., using the Stokes vector parameters as input to algorithms) by using instead the raw polarization component imagery (e.g., 0°, 45°, 90°, and 135°) and employing multivariate mathematical statistics to distinguish the two classes of objects. This dissertation unequivocally shows that algorithms based on this new direction significantly outperform the prior art (algorithms based on Stokes parameters and their variants). To support this claim, this dissertation offers an exhaustive data analysis and quantitative comparative study, among the various competing algorithms, using long-wave infrared polarimetric imagery collected outdoor, over several days, under varying weather conditions, geometry of illumination, and diurnal cycles

Passively Estimating Index of Refraction for Specular Reflectors Using Polarimetric Hyperspectral Imaging

As off-nadir viewing platforms becoming increasingly prevalent in remote sensing, material classification and ID techniques robust to changing viewing geometries must be developed. Traditionally, either reflectivity or emissivity are used for classification, but these quantities vary with viewing angle. Instead, estimating index of refraction may be advantageous as it is invariant with respect to viewing geometry. This work focuses on estimating index of refraction from LWIR (875-1250 wavenumbers) polarimetric hyperspectral radiance measurements

Development of a Spectropolarimetric Capability

A Bomem 200 series Fourier transform infrared spectrometer and FLIR Systems Inc InGaAs infrared (IR) camera was used to measure the degree of linear polarization (DOLP) from a metal and glass surfaces. This was accomplished by placing a wire grid polarizer in front of the aperture of the FTIR and IR camera to measure the polarized reflection from the target material. For the IR camera, an average of pixel counts was collected over the area of the target to measure to DOLP. Factors impacting the accuracy of the measurements from the FTIR are calibration, instrument self emission, and alignment error. Factors impacting the value of the DOLP from the material are the complex index of refraction, angle of incidence, and surface roughness. Results of these measurements match theory with varying success and also show the limitation and challenges associated with the physical limitation of the equipment used

Oblique Longwave Infrared Atmospheric Compensation

This research introduces two novel oblique longwave infrared atmospheric compensation techniques for hyperspectral imagery, Oblique In-Scene Atmospheric Compensation (OISAC) and Radiance Detrending (RD). Current atmospheric compensation algorithms have been developed for nadir-viewing geometries which assume that every pixel in the scene is affected by the atmosphere in nearly the same manner. However, this assumption is violated in oblique imaging conditions where the transmission and path radiance vary continuously as a function of object-sensor range, negatively impacting current algorithms in their ability to compensate for the atmosphere. The techniques presented here leverage the changing viewing conditions to improve rather than hinder atmospheric compensation performance. Initial analyses of both synthetic and measured hyperspectral images suggest improved performance in oblique viewing conditions compared to standard techniques. OISAC is an extension of ISAC, a technique that has been used extensively for LWIR AC applications for over 15 years, that has been developed to incorporate the range-dependence of atmospheric transmission and path radiance in identification of the atmospheric state. Similar to ISAC, OISAC requires the existence of near blackbody-like materials over the 11.73 micrometer water band. RD is a newer technique which features unsupervised classification of materials and identifies the atmospheric state which best detrends the observed radiance across all classes of materials, including those of low emissivity