Publications

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Optical remote sensing has become a valuable tool in many application spaces because it can be unobtrusive, search large areas efficiently, and is increasingly accessible through commercially available products and systems. In the application space of chemical, biological, radiological, nuclear, and explosives (CBRNE) sensing, optical remote sensing can be an especially valuable tool because it enables data to be collected from a safe standoff distance. Data products and results from remote sensing collections can be combined with results from other methods to offer an integrated understanding of the nature of activities in an area of interest and may be used to inform in-situ verification techniques. This work will overview several independent research efforts focused on developing and leveraging spectral and polarimetric sensing techniques for CBRNE applications, including system development efforts, field deployment campaigns, and data exploitation and analysis results. While this body of work has primarily focused on the application spaces of chemical and underground nuclear explosion detection and characterization, the developed tools and techniques may have applicability to the broader CBRNE domain.

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.

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Hyperspectral and multispectral imagers have been developed and deployed on satellite and manned aerial platforms for decades and have been used to produce spectrally resolved reflectance and other radiometric products. Similarly, light detection and ranging, or LIDAR, systems are regularly deployed from manned aerial platforms to produce a variety of products, including digital elevation models. While both types of systems have demonstrated impressive capabilities from these conventional platforms, for some applications it is desirable to have higher spatial resolution and more deployment flexibility than satellite or manned aerial platforms can offer. Commercially available unmanned aerial systems, or UAS, have recently emerged as an alternative platform for deploying optical imaging and detection systems, including spectral imagers and high resolution cameras. By enabling deployments in rugged terrain, collections at low altitudes, and flight durations of several hours, UAS offer the opportunity to obtain high spatial resolution products over multiple square kilometers in remote locations. Taking advantage of this emerging capability, our team recently deployed a commercial UAS to collect hyperspectral imagery, RGB imagery, and photogrammetry products at a legacy underground nuclear explosion test site and its surrounds. Ground based point spectrometer data collected over the same area serves as ground truth for the airborne results. The collected data is being used to map the site and evaluate the utility of optical remote sensing techniques for measuring signatures of interest, such as the mineralogy, anthropogenic objects, and vegetative health. This work will overview our test campaign, our results to date, and our plans for future work.

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Channeled spectropolarimetry measures the spectrally resolved Stokes parameters. A key aspect of this technique is to accurately reconstruct the Stokes parameters from a modulated measurement of the channeled spectropolarimeter. The state-of-the-art reconstruction algorithm uses the Fourier transform to extract the Stokes parameters from channels in the Fourier domain. While this approach is straightforward, it can be sensitive to noise and channel cross-talk, and it imposes bandwidth limitations that cut o high frequency details. To overcome these drawbacks, we present a reconstruction method called compressed channeled spectropolarimetry. In our proposed framework, reconstruction in channeled spectropolarimetry is an underdetermined problem, where we take N measurements and solve for 3N unknown Stokes parameters. We formulate an optimization problem by creating a mathematical model of the channeled spectropolarimeter with inspiration from compressed sensing. We show that our approach o ers greater noise robustness and reconstruction accuracy compared with the Fourier transform technique in simulations and experimental measurements. By demonstrating more accurate reconstructions, we push performance to the native resolution of the sensor, allowing more information to be recovered from a single measurement of a channeled spectropolarimeter.

Hyperspectral imaging polarimetry enables both the spectrum and its spectrally resolved state of polarization to be measured. This information is important for identifying material properties for various applications in remote sensing and agricultural monitoring. We describe the design and performance of a ruggedized, field deployable hyperspectral imaging polarimeter, designed for wavelengths spanning the visible to near-infrared (450 to 800 nm). An entrance slit was used to sample the scene in a pushbroom scanning mode across a 30 deg vertical by 110 deg horizontal field-of-view. Furthermore, athermalized achromatic retarders were implemented in a channel spectrum generator to measure the linear Stokes parameters. The mechanical and optical layout of the system and its peripherals, in addition to the results of the sensor's spectral and polarimetric calibration, are provided. Finally, field measurements are also provided and an error analysis is conducted. With its present calibration, the sensor has an absolute polarimetric error of 2.5% RMS and a relative spectral error of 2.3% RMS.

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All analysis was performed using data provided to SNL in June, 2016.

Channeled linear imaging polarimeters measure the two-dimensional distribution of the linear Stokes parameters. A key aspect of this technique is to accurately reconstruct the Stokes parameters from a snapshot, modulated measurement of the channeled linear imaging polarimeter. The state-of-The-Art reconstruction takes the Fourier transform of the measurement to separate the Stokes parameters into channels. While straightforward, this approach is sensitive to channel cross-Talk and imposes bandwidth limitations that cut off high frequency details. To overcome these drawbacks, we present a reconstruction method called compressed channeled linear imaging polarimetry. In this framework, reconstruction in channeled linear imaging polarimetry is an underdetermined problem, where we measure N pixels and recover 3N Stokes parameters. We formulate an optimization problem by creating a mathematical model of the channeled linear imaging polarimeter with inspiration from compressed sensing. Through simulations, we show that our approach mitigates artifacts seen in Fourier reconstruction, including image blurring and degradation and ringing artifacts caused by windowing and channel cross-Talk. By demonstrating more accurate reconstructions, we push performance to the native resolution of the sensor, allowing more information to be recovered from a single measurement of a channeled linear imaging polarimeter.

Detection of cultural artifacts from airborne remotely sensed data is an important task in the context of on-site inspections. Airborne artifact detection can reduce the size of the search area the ground based inspection team must visit, thereby improving the efficiency of the inspection process. This report details two algorithms for detection of cultural artifacts in aerial long wave infrared imagery. The first algorithm creates an explicit model for cultural artifacts, and finds data that fits the model. The second algorithm creates a model of the background and finds data that does not fit the model. Both algorithms are applied to orthomosaic imagery generated as part of the MSFE13 data collection campaign under the spectral technology evaluation project.

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Amorphous titania thin films were examined for use as the active material in a polarimetry based HF sensor. The amorphous titania films were found to be sensitive to vapor phase HF and the reaction product was identified as a hydronium oxofluorotitanate phase, which has previously only been synthesized in aqueous solution. The extent of reaction varied both with vapor phase HF concentration, relative humidity, and the exposure time. HF concentrations as low as 1 ppm could be detected for exposure times of 120 h.

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