Using Lidar to geometrically-constrain signature spaces for physics-based target detection

A fundamental task when performing target detection on spectral imagery is ensuring that a target signature is in the same metric domain as the measured spectral data set. Remotely sensed data are typically collected in digital counts and calibrated to radiance. That is, calibrated data have units of spectral radiance, while target signatures in the visible regime are commonly characterized in units of re°ectance. A necessary precursor to running a target detection algorithm is converting the measured scene data and target signature to the same domain. Atmospheric inversion or compensation is a well-known method for transforming mea- sured scene radiance values into the re°ectance domain. While this method may be math- ematically trivial, it is computationally attractive and is most e®ective when illumination conditions are constant across a scene. However, when illumination conditions are not con- stant for a given scene, signi¯cant error may be introduced when applying the same linear inversion globally. In contrast to the inversion methodology, physics-based forward modeling approaches aim to predict the possible ways that a target might appear in a scene using atmospheric and radiometric models. To fully encompass possible target variability due to changing illumination levels, a target vector space is created. In addition to accounting for varying illumination, physics-based model approaches have a distinct advantage in that they can also incorporate target variability due to a variety of other sources, to include adjacency target orientation, and mixed pixels. Increasing the variability of the target vector space may be beneficial in a global sense in that it may allow for the detection of difficult targets, such as shadowed or partially concealed targets. However, it should also be noted that expansion of the target space may introduce unnecessary confusion for a given pixel. Furthermore, traditional physics-based approaches make certain assumptions which may be prudent only when passive, spectral data for a scene are available. Common examples include the assumption of a °at ground plane and pure target pixels. Many of these assumptions may be attributed to the lack of three-dimensional (3D) spatial information for the scene. In the event that 3D spatial information were available, certain assumptions could be levied, allowing accurate geometric information to be fed to the physics-based model on a pixel- by-pixel basis. Doing so may e®ectively constrain the physics-based model, resulting in a pixel-specific target space with optimized variability and minimized confusion. This body of work explores using spatial information from a topographic Light Detection and Ranging (Lidar) system as a means to enhance the delity of physics-based models for spectral target detection. The incorporation of subpixel spatial information, relative to a hyperspectral image (HSI) pixel, provides valuable insight about plausible geometric con¯gurations of a target, background, and illumination sources within a scene. Methods for estimating local geometry on a per-pixel basis are introduced; this spatial information is then fed into a physics-based model to the forward prediction of a target in radiance space. The target detection performance based on this spatially-enhanced, spectral target space is assessed relative to current state-of-the-art spectral algorithms

Application of multispectral radar and LANDSAT imagery to geologic mapping in death valley

Side-Looking Airborne Radar (SLAR) images, acquired by JPL and Strategic Air Command Systems, and visible and near-infrared LANDSAT imagery were applied to studies of the Quaternary alluvial and evaporite deposits in Death Valley, California. Unprocessed radar imagery revealed considerable variation in microwave backscatter, generally correlated with surface roughness. For Death Valley, LANDSAT imagery is of limited value in discriminating the Quaternary units except for alluvial units distinguishable by presence or absence of desert varnish or evaporite units whose extremely rough surfaces are strongly shadowed. In contrast, radar returns are most strongly dependent on surface roughness, a property more strongly correlated with surficial geology than is surface chemistry

Path Planning and Evolutionary Optimization of Wheeled Robots

Probabilistic roadmap methods (PRM) have been a well-known solution for solving motion planning problems where we have a fixed set of start and goal configurations in a workspace. We define a configuration space with static obstacles. We implement PRM to find a feasible path between start and goal for car-like robots. We further extend the concept of path planning by incorporating evolutionary optimization algorithms to tune the PRM parameters. The theory is demonstrated with simulations and experiments. Our results show that there is a significant improvement in the performance metrics of PRM after optimizing the PRM parameters using biogeography-based optimization, which is an evolutionary optimization algorithm. The performance metrics (namely path length, number of hops, number of loops and fail-rate) show 34.91 , 23.18 , 52.21 and 21.21 improvement after using optimized PRM parameters. We also experimentally demonstrate the application of path planning using PRM to mobile car-like robot

In-Vivo Three Dimensional Proton Hadamard Spectroscopic Imaging in the Human Brain

Magnetic resonance spectroscopic imaging (MRSI) is a useful tool for obtaining information on the biochemical processes underlying various pathologies. A widely used multi-voxel localization method is chemical shift imaging (CSI) which uses gradients for phase encoding. Although simple to implement, low in specific absorption rate (SAR) and immune to chemical shift displacement (CSD), it also suffers from some well known drawbacks caused by its sinc-shaped point spread function (PSF). This results in loss of both signal-to-noise ratio (SNR) as well as localization, an effect that is exacerbated at low resolutions. In contrast, an alternative localization method, Hadamard spectroscopic imaging (HSI) benefits from a theoretically ideal PSF and consequently does not suffer from these drawbacks. In this work we exploit the theoretically ideal PSF of HSI encoding to develop a novel three dimensional (3D) multi-voxel MR localization method based on transverse HSI (T-HSI). The advantages of T HSI are that unlike gradient phase-encoding: (i) the volume of interest (VOI) does not need to be smaller than the field-of-view to prevent aliasing; (ii) the number of partitions in each direction can be small, 8, 4 or even 2 at no cost in PSF; (iii) the VOI does not have to be contiguous; and (iv) the voxel profile depends on the available B1 and pulse synthesis paradigm and can therefore, at least theoretically, approach "ideal" "1" inside and "0" elsewhere. Clinical utility of the new method is shown by spectra obtained from the brain of a healthy volunteer. The benefits of T-HSI are demonstrated by a quantitative comparison to CSI of the SNR and localization in a phantom in both one and three dimensions at clinical resolutions. A novel matrix formalism is used to quantify the impact of non-ideal flip angles on T-HSI. The superior PSF of T-HSI is then used to demonstrate the feasibility of scanning regions near or on the skull while limiting the impact of lipid contamination and obtaining quantifiable spectra. A comparison to spectra obtained using CSI is shown for a healthy volunteer. The new method is also used in a clinical pathology: to scan multiple sclerosis (MS) lesions occurring near the skull. To maintain the benefits provided by the PSF of HSI at higher fields, despite its susceptibility to CSD, a additional hybrid sequence is also developed that limits both the SAR and the CSD, regardless of the size of the VOI. A comparison to CSI in a phantom and in-vivo is carried out and spectra obtained from the brain of a healthy volunteer at 3T are shown. Finally, future research avenues involving extension of this research to ultra high fields (7T) are discussed and possible clinical uses are described

Path Planning and Evolutionary Optimization of Wheeled Robots

Probabilistic roadmap methods (PRM) have been a well-known solution for solving motion planning problems where we have a fixed set of start and goal configurations in a workspace. We define a configuration space with static obstacles. We implement PRM to find a feasible path between start and goal for car-like robots. We further extend the concept of path planning by incorporating evolutionary optimization algorithms to tune the PRM parameters. The theory is demonstrated with simulations and experiments. Our results show that there is a significant improvement in the performance metrics of PRM after optimizing the PRM parameters using biogeography-based optimization, which is an evolutionary optimization algorithm. The performance metrics (namely path length, number of hops, number of loops and fail-rate) show 34.91 , 23.18 , 52.21 and 21.21 improvement after using optimized PRM parameters. We also experimentally demonstrate the application of path planning using PRM to mobile car-like robot

Visible and Hyperspectral Imaging Systems for the Detection and Discrimination of Mechanical and Microbiological Damage of Mushrooms

Horticultural products such as mushrooms are exposed to environmental conditions during their postharvest life, which may affect product quality. Loss of whiteness during storage is particularly important in the mushroom industry. Rough handling and distribution, fruiting body senescence and bacterial infections are among the main causes of mushroom discolouration. The aim of this work was to study the use of visible and hyperspectral imaging (HSI) systems for the detection and discrimination of mechanical and microbiological damage of mushrooms. This piece of research involved a) monitoring the browning of mushroom with visible computer imaging systems, b) investigating the effect of mechanical damage on the kinetics of enzymes responsible for mushroom browning, c) exploring the potential use of Vis-NIR HSI to predict PPO activity in mushroom caps and d) studying the potential application of Vis-NIR HSI for microbial and viral detection on mushroom caps and for their discrimination from mechanical damage. Results presented in this thesis show that the efficacy of commercial webcams was limited in the detection of mechanical damage on mushroom caps. Damage increased the activity of PPOs on mushroom pileipellis, but the effect of the extent of damage was not significant at the levels of study. Vis-NIR HSI showed some potential as a tool to estimate the activity of PPO enzymes on mushroom caps. The combination of HSI with chemometric tools allowed for the differentiation of mechanically and microbiologically damaged mushroom classes. Results from this study could be used for developing non-destructive monitoring systems for mechanical and microbiological damage detection and discrimination. The potential application of such systems as on-line process analytical tools would facilitate rapid assessment of mushroom quality.

Plasmon excitations in metallic nanostructures

A new hyperspectral imaging technique and apparatus for imaging plasmon excitations and cathodoluminescence in nanostructures with nanoscale resolution have been developed. The apparatus, based on a scanning electron microscope synchronized with a multi-channel spectrum analyzer, allows for collection and detection of optical electron-induced emission from a sample in two configurations (high efficiency and high angular resolution modes) and in the wavelength region from 350 to 1150 nm with 0.8 nm spectral resolution and high quantum efficiency.Using this instrument it was demonstrated that the injection of a beam of free electrons into an unstructured metal surface creates a highly localized nanoscale source of SPPs. It was shown that on a gold surface a 50 keV electron beam of 10 μA current creates a 10 nW source of plasmons with the spectrum spreading from 350 to 1150 nm. The plasmons were detected by controlled decoupling into light with a grating at a distance from the excitation point. The 30 nm delocalization of the plasmon source at the grating was demonstrated and decay lengths of SPPs were measured.The hyperspectral imaging technique was used to study plasmon emission, induced by an electron beam excitation on gold monocrystal decahedronshaped nanoparticles and dimers consisting of such nanoparticles. It was shown that in 100 nm gold decahedron-shaped nanoparticles electron-induced plasmon emission is excited in the spectral range from 350 to 850 nm. The dependence of spatial and spectral structure of dimer plasmon emission on wavelength and separation between the nanoparticles within the dimer was studied. The excitation of hybridized mode on a dimer with a 50 nm gap between the particles was detected at wavelength 600 nm. It was demonstrated that the electromagnetic field structure near a plasmonic nanoparticle forms a vortex. It was shown that the power-flow lines of linear polarized monochromatic light interacting with a metal lambda/20 nanoparticle, in the proximity of its plasmon resonance, form whirlpool-like nanoscale optical vortices (optical whirlpools). Both spherical and spheroidal particles were studied using analytical Mie theory and the Finite Element method. One of two types of vortices, inward or outward, was observed depending on the sign of frequency detuning between the external field and plasmon resonance of the nanoparticle

The Characterization of Earth Sediments using Radiative Transfer Models from Directional Hyperspectral Reflectance

Remote sensing techniques are continuously being developed to extract physical information about the Earth’s surface. Over the years, space-borne and airborne sensors have been used for the characterization of surface sediments. Geophysical properties of a sediment surface such as its density, grain size, surface roughness, and moisture content can influence the angular dependence of spectral signatures, specifically the Bidirectional Reflectance Distribution Function (BRDF). Models based on radiative transfer equations can relate the angular dependence of the reflectance to these geophysical variables. Extraction of these parameters can provide a better understanding of the Earth’s surface, and play a vital role in various environmental modeling processes. In this work, we focused on retrieving two of these geophysical properties of earth sediments, the bulk density and the soil moisture content (SMC), using directional hyperspectral reflectance. We proposed a modification to the radiative transfer model developed by Hapke to retrieve sediment bulk density. The model was verified under controlled experiments within a laboratory setting, followed by retrieval of the sediment density from different remote sensing platforms: airborne, space-borne and a ground-based imaging sensor. The SMC was characterized using the physics based multilayer radiative transfer model of soil reflectance or MARMIT. The MARMIT model was again validated from experiments performed in our controlled laboratory setting using several different soil samples across the United States; followed by applying the model in mapping SMC from imagery data collected by an Unmanned Aerial System (UAS) based hyperspectral sensor