Publications

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We investigate the feasibility of additively manufacturing optical components to accomplish task-specific classification in a computational imaging device. We report on the design, fabrication, and characterization of a non-traditional optical element that physically realizes an extremely compressed, optimized sensing matrix. The compression is achieved by designing an optical element that only samples the regions of object space most relevant to the classification algorithms, as determined by machine learning algorithms. The design process for the proposed optical element converts the optimal sensing matrix to a refractive surface composed of a minimized set of non-repeating, unique prisms. The optical elements are 3D printed using a Nanoscribe, which uses two-photon polymerization for high-precision printing. We describe the design of several computational imaging prototype elements. We characterize these components, including surface topography, surface roughness, and angle of prism facets of the as-fabricated elements.

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We report on the design and fabrication of a computational imaging element used within a compressive task-specific imaging system. Fabrication via two-photon 3D printing is reported, as well as characterization of the fabricated element.

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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Compact snapshot imaging polarimeters have been demonstrated in literature to provide Stokes parameter estimations for spatially varying scenes using polarization gratings. However, the demonstrated system does not employ aggressive modulation frequencies to take full advantage of the bandwidth available to the focal plane array. A snapshot imaging Stokes polarimeter is described and demonstrated through results. The simulation studies the challenges of using a maximum bandwidth configuration for a snapshot polarization grating based polarimeter, such as the fringe contrast attenuation that results from higher modulation frequencies. Similar simulation results are generated and compared for a microgrid polarimeter. Microgrid polarimeters are instruments where pixelated polarizers are superimposed onto a focal plan array, and this is another type of spatially modulated polarimeter, and the most common design uses a 2x2 super pixel of polarizers which maximally uses the available bandwidth of the focal plane array.