Atmospheric fogs create degraded visual environments, making it difficult to recover optical information from our surroundings. We have developed a low-SWaP technique which characterizes these environments using an f-theta lens to capture the angular scattering profile of a pencil beam passed through a fog. These measurements are then compared to data taken in tandem by conventional characterization techniques (optical transmission, bulk scattering coefficient, etc.). We present this angular scattering measurement as a low-SWaP alternative to current degraded visual environment characterization techniques to provide real-time data for implementation with signal recovery algorithms.
In this paper, we develop a nested chi-squared likelihood ratio test for selecting among shrinkage-regularized covariance estimators for background modeling in hyperspectral imagery. Critical to many target and anomaly detection algorithms is the modeling and estimation of the underlying background signal present in the data. This is especially important in hyperspectral imagery, wherein the signals of interest often represent only a small fraction of the observed variance, for example when targets of interest are subpixel. This background is often modeled by a local or global multivariate Gaussian distribution, which necessitates estimating a covariance matrix. Maximum likelihood estimation of this matrix often overfits the available data, particularly in high dimensional settings such as hyperspectral imagery, yielding subpar detection results. Instead, shrinkage estimators are often used to regularize the estimate. Shrinkage estimators linearly combine the overfit covariance with an underfit shrinkage target, thereby producing a well-fit estimator. These estimators introduce a shrinkage parameter, which controls the relative weighting between the covariance and shrinkage target. There have been many proposed methods for setting this parameter, but comparing these methods and shrinkage values is often performed with a cross-validation procedure, which can be computationally expensive and highly sample inefficient. Drawing from Bayesian regression methods, we compute the degrees of freedom of a covariance estimate using eigenvalue thresholding and employ a nested chi-squared likelihood ratio test for comparing estimators. This likelihood ratio test requires no cross-validation procedure and enables direct comparison of different shrinkage estimates, which is computationally efficient.
Event-based sensors are a novel sensing technology which capture the dynamics of a scene via pixel-level change detection. This technology operates with high speed (>10 kHz), low latency (10 μs), low power consumption (<1 W), and high dynamic range (120 dB). Compared to conventional, frame-based architectures that consistently report data for each pixel at a given frame rate, event-based sensor pixels only report data if a change in pixel intensity occurred. This affords the possibility of dramatically reducing the data reported in bandwidth-limited environments (e.g., remote sensing) and thus, the data needed to be processed while still recovering significant events. Degraded visual environments, such as those generated by fog, often hinder situational awareness by decreasing optical resolution and transmission range via random scattering of light. To respond to this challenge, we present the deployment of an event-based sensor in a controlled, experimentally generated, well-characterized degraded visual environment (a fog analogue), for detection of a modulated signal and comparison of data collected from an event-based sensor and from a traditional framing sensor.
Atmospheric fog is a common degraded visual environment (DVE) that reduces sensing and imaging range and resolution in complex ways not fully encapsulated by traditional metrics. As such, better physical models are required to describe imaging systems in a fog environment. We have developed a tabletop fog chamber capable of creating repeatable fog-like environments for controlled experimentation of optical systems within this common DVE. We present measurement of transmission coefficients and droplet size distribution in a multiple scattering regime using this chamber.
Degraded visual environments like fog pose a major challenge to safety and security because light is scattered by tiny particles. We show that by interpreting the scattered light it is possible to detect, localize, and characterize objects normally hidden in fog. First, a computationally efficient light transport model is presented that accounts for the light reflected and blocked by an opaque object. Then, statistical detection is demonstrated for a specified false alarm rate using the Neyman-Pearson lemma. Finally, object localization and characterization are implemented using the maximum likelihood estimate. These capabilities are being tested at the Sandia National Laboratory Fog Chamber Facility.
Identification of vegetation species and type is important in many chemical, biological, radiological, nuclear, and explosive sensing applications. For instance, emergence of non-climax species in an area may be indicative of anthropogenic activity which can complement prompt signatures for underground nuclear explosion detection and localization. To explore signatures of underground nuclear explosions, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of 274 visible and near-infrared wavebands over 4.3 km2 of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. Previous work has shown that a vegetation spectral derivative can be more indicative of species than the measured value of each band. However, applying a spectral derivative amplifies any noise in the spectrum and reduces the benefit of the derivative analysis. Fitting the spectra with a polynomial can provide the slope information (derivative) without amplifying noise. In this work, we simultaneously capture slope and curvature information and reduce the dimensionality of remotely sensed hyperspectral imaging data. This is performed by employing a 2nd order polynomial fit across spectral bands of interest. We then compare the classification accuracy of a support vector machine classifier fit to the polynomial dimensionality reduction technique and the same support vector machine fit to the same number of components from principle component analysis.
This communication reports progress towards the development of computational sensing and imaging methods that utilize highly scattered light to extract information at greater depths in degraded visual environments like fog for improved situational awareness. As light propagates through fog, information is lost due to random scattering and absorption by micrometer sized water droplets. Computational diffuse optical imaging shows promise for interpreting the detected scattered light, enabling greater depth penetration than current methods. Developing this capability requires verification and validation of diffusion models of light propagation in fog. We report models that were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.
Degraded visual environments are a cause of problems for surveillance systems and other sensors due to the reduction in contrast, range, and signal. Fog is a concern because of the frequency of its formation along our coastlines; disrupting border security and surveillance. Sandia has created a Fog Facility for the characterization and testing of optical and other systems. We will present a comparison of our generated fogs to the measured naturally occurring fogs reported in the literature and an overview of Sandia’s work using this facility to investigate ways to enhance perception through degraded visual environments.
There are several factors that should be considered for robust terrain classification. We address the issue of high pixel-wise variability within terrain classes from remote sensing modalities, when the spatial resolution is less than one meter. Our proposed method segments an image into superpixels, makes terrain classification decisions on the pixels within each superpixel using the probabilistic feature fusion (PFF) classifier, then makes a superpixel-level terrain classification decision by the majority vote of the pixels within the superpixel. We show that this method leads to improved terrain classification decisions. We demonstrate our method on optical, hyperspectral, and polarimetric synthetic aperture radar data.
The detection, location, and identification of suspected underground nuclear explosions (UNEs) are global security priorities that rely on integrated analysis of multiple data modalities for uncertainty reduction in event analysis. Vegetation disturbances may provide complementary signatures that can confirm or build on the observables produced by prompt sensing techniques such as seismic or radionuclide monitoring networks. For instance, the emergence of non-native species in an area may be indicative of anthropogenic activity or changes in vegetation health may reflect changes in the site conditions resulting from an underground explosion. Previously, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of visible and near-infrared wavebands over 4.3 km2 of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. In this work, we employ various spectral detection and classification algorithms to identify and map vegetation species in an area of interest containing the legacy test site. We employed a frequentist framework for fusing multiple spectral detections across various reference spectra captured at different times and sampled from multiple locations. The spatial distribution of vegetation species is compared to the location of the underground nuclear explosion. We find a difference in species abundance within a 130 m radius of the center of the test site.
Heavy fogs and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on a range of optical sensors for guidance and remote sensing that can be degraded by highly scattering environments. In our previous and on-going simulation work, we have shown polarized light can increase signal or range through a scattering environment such as fog. Specifically, we have shown circularly polarized light maintains its polarized signal through a larger number of scattering events and thus range, better than linearly polarized light. In this work we present design and testing results of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imager, focusing on linear and circular polarization states. Testing of the imager was performed in the Sandia Fog Facility. The Sandia Fog Facility is a 180 ft. by 10 ft. chamber that can create fog-like conditions for optical testing. This facility offers a repeatable fog scattering environment ideally suited to test the imager’s performance in fog conditions. We show that circular polarized imagers can penetrate fog better than linear polarized imagers.
Fog is a commonly occurring degraded visual environment which disrupts air traffic, ground traffic, and security imaging systems. For many application of interest, spatial resolution is required to identify elements of the scene. However, studying the effects of fog on resolution degradation is difficult because the composition of naturally occurring fogs is variable, and data collection is reliant on changing weather conditions. For our study, we used the Sandia National Laboratories fog facility to generate repeatable characterized fog conditions. Sandia’s well characterized fog generation allowed us to relate the resolution degradation of active and passive long-wave infrared (LWIR) imagers to the properties of fog. Additionally, the fogs we generated were denser than naturally occurring fogs. This allowed for testing of long range imaging in the shorter optical pathlengths obtainable in a laboratory environment.
In this presentation, we experimentally investigate the resolution degradation of LWIR wavelengths in realistic fog droplet sizes. Transmission of LWIR wavelengths has been studied extensively in literature. To date however, there are few experimental results quantifying the resolution degradation for LWIR imagery in fog. We present experimental results on resolution degradation for both passive and active LWIR systems. The degradation of passive imaging was measured using 37˚C blackbody with a slant edge resolution targets. The active imaging resolution degradation was measured using a polarized CO2 laser reflecting off a set of bar targets. We found that the relationship between meteorological optical range and resolution degradation was more complicated than described purely by attenuation.
The scattering of light in fog is a complex problem that affects imaging in many ways. Typically, imaging device performance in fog is attributed solely to reduced visibility measured as light extinction from scattering events. We present a quantitative analysis of resolution degradation in the long-wave infrared regime. Our analysis is based on the calculation of the modulation transfer function from the edge response of a slant edge blackbody target in known fog conditions. We show higher spatial frequencies attenuate more than low spatial frequencies with increasing fog thickness. These results demonstrate that image blurring, in addition to extinction, contributes to degraded performance of imaging devices in fog environments.
We report on the design, modeling, calibration, and experimental results of a LWIR, spectrally and temporally resolved broad band bi-directional reflectance distribution function measuring device. The system is built using a commercial Fourier transform infrared spectrometer, which presents challenges due to relatively low power output compared to laser based methods. The instrument is designed with a sample area that is oriented normal to gravity, making the device suitable for measuring loose powder materials, liquids, or other samples that can be difficult to measure in a vertical orientation. The team built a radiometric model designed to understand the trade space available for various design choices as well as to predict instrument success at measuring the target materials. The radiometric model was built by using the output of commercial non sequential raytracing tools combined with a scripted simulation of the interferometer. The trade space identified in this analysis will be presented.
The design was based on moving periscopes with custom off axis parabolas to focus the light onto the sample. The system assembly and alignment will be discussed. The calibration method used for the sensor will be detailed, and preliminary measurements from this research sensor will be presented.
Heavy fogs and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on a range of optical sensors for guidance and remote sensing that can be degraded by highly scattering environments. In our previous and on-going simulation work, we have shown polarized light can increase signal or range through a scattering environment such as fog. Specifically, we have shown circularly polarized light maintains its polarized signal through a larger number of scattering events and thus range, better than linearly polarized light. In this work we present an active polarization imager in the short-wave infrared. We explore multiple polarimetric configurations for the imager, focusing on linear and circular polarization states. We also describe initial testing of the imager in the Sandia Fog Facility. The Sandia Fog Facility is a 180 ft. by 10 ft. chamber that can create fog-like conditions for optical testing. This facility offers a repeatable fog scattering environment ideally suited to test the imager’s performance in fog conditions.
Degraded visual environments are a cause of problems for surveillance systems and other sensors due to the reduction in contrast, range, and signal. Fog is a concern because of the frequency of its formation along our coastlines; disrupting border security, shipping, surveillance, and sometimes causing deadly accidents. Fog reduces visibility by scattering ambient/active illumination light obscuring the environment and limiting operational capability. Sandia has created a fog facility for the characterization and testing of optical and other systems. This facility is a 180 ft. by 10 ft. by 10 ft. chamber with temperature control that can be filled with a fog-like aerosol using 64 agricultural spray nozzles. We will discuss the physical formation of fog and how that is affected by the environmental controls at our disposal. We have recently made several improvements to the facility including temperature control and will present the results of these improvements on the aerosol conditions. We will discuss the characterization of the fog and instrumentation used for the characterization. In addition, we will present preliminary results from work at Sandia, that leveraged this facility to investigate using polarized light to enhance the range of optical systems in fog conditions. This capability provides a platform for performing optical propagations experiments in a known, stable, and controlled environment where fog can be made on demand.
Degraded visual environments are a serious concern for modern sensing and surveillance systems. Fog is of interest due to the frequency of its formation along our coastlines disrupting border security and surveillance. Fog presents hurdles in intelligence and reconnaissance by preventing data collection with optical systems for extended periods. We will present recent results from our work in operating optical systems in our controlled fog experimental chamber. This facility is a 180-foot-long, 10-foot-wide, and 10-foot-tall structure that has over 60 spray nozzles to achieve uniform aerosol coverage with various particle size, distributions, and densities. We will discuss the physical formation of fog in nature and how our generated fog compares. In addition, we will discuss fog distributions and characterization techniques. We will investigate the biases of different methods and discuss the different techniques that are appropriate for realistic environments. Finally, we will compare the data obtained from our characterization studies against accepted models (e.g., MODTRAN) and validate the usage of this unique capability as a controlled experimental realization of natural fog formations. By proving the capability, we will enable the testing and validation of future fog penetrating optical systems and providing a platform for performing optical propagation experimentation in a known, stable, and controlled environment.
Scattering environment conditions, such as fog, pose a challenge for many detection and surveillance active sensing operations in both ground and air platforms. For example, current autonomous vehicles rely on a range of optical sensors that are affected by degraded visual environments. Real-world fog conditions can vary widely depending on the location and environmental conditions during its creation. In our previous work we have shown benefits for increasing signal and range through scattering environments such as fog utilizing polarized light, specifically circular polarization. In this work we investigate the effect of changing fog particle sizes and distributions on polarization persistence for both circularly and linearly polarized light via simulation. We present polarization tracking Monte Carlo results for a range of realistic monodisperse particle sizes as well as varying particle size distributions as a model of scattering environments. We systematically vary the monodisperse particle size, mean particle size of a distribution, particle size distribution width, and number of distribution lobes (bi-modal), as they affect polarized light transmission through a scattering environment. We show that circular polarization signal persists better than linear polarization signal for most variations of the particle distribution parameters.
We present experimental and simulation results for a laboratory-based forward-scattering environment, where 1 μm diameter polystyrene spheres are suspended in water to model the optical scattering properties of fog. Circular polarization maintains its degree of polarization better than linear polarization as the optical thickness of the scattering environment increases. Both simulation and experiment quantify circular polarization’s superior persistence, compared to that of linear polarization, and show that it is much less affected by variations in the field of view and collection area of the optical system. Our experimental environment’s lateral extent was physically finite, causing a significant difference between measured and simulated degree of polarization values for incident linearly polarized light, but not for circularly polarized light. Through simulation we demonstrate that circular polarization is less susceptible to the finite environmental extent as well as the collection optic’s limiting configuration.
We present simulation results that show circularly polarized light persists through scattering environments better than linearly polarized light. Specifically, we show persistence is enhanced through many scattering events in an environment with a size parameter representative of advection fog at infrared wavelengths. Utilizing polarization tracking Monte Carlo simulations we show a larger persistence benefit for circular polarization versus linear polarization for both forward and backscattered photons. We show the evolution of the incident polarization states after various scattering events which highlight the mechanism leading to circular polarization’s superior persistence.
The development of organic polymers with low infrared absorption has been investigated as a possible alternative to inorganic metal oxide, semiconductor, or chalcogenide-based materials for a variety of optical devices and components, such as lenses, goggles, thermal imaging cameras and optical fibers. In principle, organic-based polymers are attractive for these applications because of their low weight, ease of processing, mechanical toughness, and facile chemical variation using commercially available precursors. Herein we report on the optical characterization of a new class of sulfur copolymers that are readily moldable, transparent above 500 nm, possess high refractive index (n > 1.8) and take advantage of the low infrared absorption of S-S bonds for potential use in the mid-infrared at 3-5 microns. These materials are largely made from elemental sulfur by an inverse vulcanization process; in the current study we focus on the properties of a chemically stable, branched copolymer of poly(sulfur-random-1,3-diisopropenylbenzene) (poly(S-r- DIB). Copolymers with elemental sulfur content ranging from 50% to 80% by weight were studied by UV-VIS spectroscopy, FTIR, and prism coupling for refractive index measurement. Clear correlation between material composition and the optical properties was established, confirming that the high polarizability of the sulfur atom leads to high refractive index while also maintaining low optical loss in the infrared.
We present both simulation and experimental results showing that circularly polarized light maintains its degree of
polarization better than linearly polarized light in scattering environments. This is specifically true in turbid
environments like fog and clouds. In contrast to previous studies that propagate single wavelengths through broad
particle-size distributions, this work identifies regions where circular polarization persists further than linear by
systematically surveying different wavelengths through monodisperse particle diameters. For monodisperse polystyrene
microspheres in water, for particle diameters of 0.99 and 1.925 microns and varying optical depths, we show that circular
polarization’s ability to persist through multiple scattering events is enhanced by as much as a factor of four, when
compared to that of linear polarization. These particle diameters correspond to size parameters found for infrared
wavelengths and marine and continental fog particle distributions. The experimental results are compared to Monte
Carlo simulations for all scattering environments investigated.
We find for infrared wavelengths there are clear particle size ranges and indices representative of fog and rain where the use of circular polarization imaging can penetrate to larger optical depths than linear polarization. Using polarization tracking Monte Carlo simulations for varying particle size, wavelength, and index systematically, we show that for specific scene parameters circular polarization vastly outperforms linear polarization in maintaining degree of polarization for large optical depths in transmission and reflection. This enhancement in circular polarization can be exploited to improve imaging in obscurant environments that are important in many critical imaging applications. Specifically, circular polarization performs better than linear for radiation fog in the SWIR and MWIR regime, advection fog in the LWIR regime, and small sized particles of Sahara dust in the MWIR regime.
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