Photon-sensitive lidar receivers enable range measurements at high probability of detection and low false alarm rate using only 5 - 10 detected photons on average per range measurement. This much-reduced link requirement, compared to photodiodes operating in linear mode, holds the promise of much-reduced system volume, mass, and power consumption, while simultaneously enabling longer standoff and higher measurement rates. We present a commercially-available, Geiger-mode lidar system, called Zion, optimized for rapid collection of dense 3D point clouds using small, economical aircraft. The system mass is under 120 kg and it consumes under 1 kW. Zion has operated at ranges between 800 m and 8,000 m. The area collection rate for data products with density of 100 points per square meter exceeds 300 km2/hr at an aircraft altitude of 1,400 m. The maximum usable measurement rate exceeds 10 million points per second. A significant capability of Zion is the agile geo-referenced scanning system, which can point and scan anywhere within a 40 × 40 degree field of regard. Collection efficiency is optimized by scanning only the desired geographic region of interest (e.g. meandering roads and utility corridors) and even in spite of non-ideal aircraft flight path and attitude. The agile, georeferenced scanning allows the flexibility to maximize oblique imaging of structures or to penetrate dense foliage. The collected points are spread evenly across the imaged area, which reduces image artifacts and simplifies processing. This system has flown over 50 flights, and is currently operational.
Aerial lidar systems tend to have narrow instantaneous fields of view, with imagers ranging from a single pixel to many tens of thousands of pixels. To collect data over a large area, the narrow lidar field of view (FoV) must be scanned. We present a unique method of scanning a lidar FoV that provides significant flexibility and allows uniform ground coverage, concentrating the system capability only in areas of interest. This method uses a queue of convex polygons, specified in world coordinates. Pre-collection planning tools establish the polygon layout. In flight, the lidar system adaptively collects those polygons that are inside the sensor field of regard, rapidly switching among the polygons as the aircraft flies. This scanning method enables the lidar to accomplish repeated collections of a single target or collections that cover a long straight or meandering path. It also enables collection of corridors with irregular widths, such as power line corridors with bulges at municipal power sub-stations or rail or roadway intersections. In the case of mixed scene types, the system can concentrate more collection time on foliated regions relative to unfoliated regions. Angular diversity can be achieved by sequentially revisiting a single target polygon. Live target tasking is accomplished by adding new targets to the target queue without stopping an ongoing collection. We present scanning simulations and example lidar data collected in flight with this scanning strategy and show some examples of sampling uniformity under the finite bandwidth and acceleration of a real scanning system.
Results are presented for the detection of trace explosive residues on real-world surfaces using active mid-infrared (MIR) hyperspectral imaging. The target surface is illuminated using miniature, rapidly tunable, external-cavity quantum cascade lasers (EC-QCLs) and the reflected light is imaged using a HgCdTe camera with a spatial resolution of 70 μm. Hypercubes with 128x128 pixels are captured with more than 256 wavelengths that span 7.7 – 11.8 μm. The samples consisted of PETN residues which were applied to keyboard keys at various levels of chemical loading. We estimate a limit of detection of less than 6 ng per pixel for the as-deposited chemical. The explosive residue remains detectable by HSI even after wiping the surface several times using isopropyl alcohol. Simple signature models for solid particles (i.e., Mie scattering) and thin-films account for the many of the spectral features observed in the chemical signatures.
A standoff chemical detection system is being developed to detect and identify a wide range of trace chemicals on a variety of natural and artificial surfaces. The system is based on active mid-infrared (MIR) hyperspectral imaging in which the target surface is illuminated using miniature, rapidly tunable, external-cavity quantum cascade lasers (ECQCLs). These lasers are tuned across the wavelength range of 7.7 – 11.8 μm while a HgCdTe camera captures images of the reflected light. Hypercubes with 128x128 pixels and more than 130 wavelengths are captured within 0.1 s. By operating the camera in sub-window mode, hypercubes with 16x96 pixels and 138 frames are captured in only 14 ms. To the best of our knowledge, these represent the world’s fastest acquisition of active MIR hypercubes. Raster-scanning of the laser beam is used to scan large regions. In this talk, we will present results for detecting traces of solid chemicals (with loadings on the order of 100 μg) on natural outdoor surfaces such as roofing shingles, concrete, sand, and asphalt at a standoff distance of 5 m. The measured spectra are found to correlate very well with those of reference measurements made of pure chemicals after accounting for the substrate reflectance.
Laser-based, mid-infrared (MIR) hyperspectral imaging (HSI) has the potential to detect a wide range of trace chemicals on a variety of surfaces under standoff conditions. The major challenge of MIR reflection spectroscopy is that the reflection signatures for surface chemicals can be complex and exhibit significant spectral variability. This paper describes a MIR Hyperspectral Simulator that is being developed to model the reflectance signatures from surfaces including the effects of speckle and other sources of spectral variability. Simulated hypercubes will be compared with experiments.
Algorithms for standoff detection and estimation of trace chemicals in hyperspectral images in the IR band are a key component for a variety of applications relevant to law-enforcement and the intelligence communities. Performance of these methods is impacted by the spectral signature variability due to presence of contaminants, surface roughness, nonlinear dependence on abundances as well as operational limitations on the compute platforms. In this work we provide a comparative performance and complexity analysis of several classes of algorithms as a function of noise levels, error distribution, scene complexity, and spatial degrees of freedom. The algorithm classes we analyze and test include adaptive cosine estimator (ACE and modifications to it), compressive/sparse methods, Bayesian estimation, and machine learning. We explicitly call out the conditions under which each algorithm class is optimal or near optimal as well as their built-in limitations and failure modes.
We report on a standoff chemical detection system using widely tunable external-cavity quantum cascade lasers (ECQCLs) to illuminate target surfaces in the mid infrared (λ = 7.4 – 10.5 μm). Hyperspectral images (hypercubes) are acquired by synchronously operating the EC-QCLs with a LN2-cooled HgCdTe camera. The use of rapidly tunable lasers and a high-frame-rate camera enables the capture of hypercubes with 128 x 128 pixels and >100 wavelengths in <0.1 s. Furthermore, raster scanning of the laser illumination allowed imaging of a 100-cm2 area at 5-m standoff. Raw hypercubes are post-processed to generate a hypercube that represents the surface reflectance relative to that of a diffuse reflectance standard. Results will be shown for liquids (e.g., silicone oil) and solid particles (e.g., caffeine, acetaminophen) on a variety of surfaces (e.g., aluminum, plastic, glass). Signature spectra are obtained for particulate loadings of RDX on glass of <1 μg/cm2.
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