The Nano-satellite Atmospheric Chemistry Hyperspectral Observation System (NACHOS) is a high-throughput (f/2.9), high spectral resolution (~1.3 nm optical resolution, 0.6 nm sampling) Offner-design hyperspectral imager operating in the 300-500 nm spectral region. The 1.5U instrument payload (1U optical system, 0.5U electronics module) is hosted by a 1.5U LANL-designed CubeSat bus to comprise a 3U complete satellite. Spectroscopically similar to NASA’s Ozone Monitoring Instrument (OMI), which provides wide-field global mapping of ozone and other gases at coarse spatial resolution, NACHOS fills the complementary niche of targeted measurements at much higher spatial resolution. With 350 across-track spatial pixels and a 15-degree across-track field of view, NACHOS will provide spectral imaging at roughly 0.4 km per pixel from 500 km altitude. NACHOS incorporates highly streamlined gas-retrieval algorithms for rapid onboard processing, alleviating the need to routinely downlink massive hyperspectral data cubes. We will discuss the instrument design, challenges in achieving mechanical robustness to launch vibration in such a compact instrument, the onboard calibration system, and gas-retrieval data downlink strategy. We will also discuss potential science missions, including monitoring of NO2 as an easily detected proxy for anthropogenic fossil-fuel greenhouse gases, monitoring lowlevel SO2 degassing at pre-eruptive volcanoes, H2CO from wildfires, and characterization of aerosols. The long-term vision is for a many-satellite constellation that could provide both high spatial resolution and frequent revisits for selected targets of interest. As an initial technology demonstration of this vision, the NACHOS project is currently slated to launch two CubeSats in early 2022.
Hyperspectral imaging with sufficient resolution and sensitivity for scientifically useful space-based mapping of trace gases has long required large and expensive satellite instruments. Miniaturizing this capability to a CubeSat configuration is a major challenge, but opens up more agile and far less expensive observing strategies. A major step in this direction is our development of NACHOS, an ultra-compact (1.5U instrument, 3U complete CubeSat) hyperspectral imager covering the 300-500nm spectral range in 400 channels. Here we describe laboratory and field performance characterization of this new instrument. Laboratory tests demonstrate spatial and spectral resolutions of <0.8 mrad and 1.3 nm, respectively, with good resolution of the spectral lines of our SO2 and NO2 target gases. Outdoor field tests under realistic illumination conditions provide real-world signal-to-noise benchmarks, and yield hyperspectral images displaying high quality solar and atmospheric spectra. To estimate on-orbit gas retrieval sensitivities, we computationally implanted plumes of varying concentrations into acquired hyperspectral datacubes. Applying our adaptive matched filter gas-retrieval algorithms to the generated scene, we predict NACHOS will be able to distinguish 35 and 7 ppm⋅m plumes of SO2 and NO2 (respectively) with high sensitivity; a capability well-suited to address scientific goals related to monitoring both passive SO2 degassing from volcanoes and NO2 emissions from anthropogenic sources. Lastly, we will show findings from thermal and vibrational environmental tests, performed in preparation for a scheduled early-2022 launch, demonstrating the extremely robust spectrometer design is well-suited for satellite-based deployment.
We describe the development and implementation of plume detection algorithms under severe bandwidth and processing constraints imposed by a CubeSat architecture. In particular, two ideas will be presented: one employs onboard processing to reduce the data that is downlinked, and one employs the Sparse Matrix Transform (SMT) to speed up the onboard computation of an approximate Mahalanobis distance.
KEYWORDS: Sensors, Signal detection, Electronic filtering, Digital filtering, Nonlinear filtering, Data modeling, Target detection, Linear filtering, Gaussian filters, Nonlinear optics
For known signals that are linearly superimposed on gaussian backgrounds, the linear adaptive matched filter (AMF) is well-known to be the optimal detector. The AMF has furthermore proved to be remarkably effective in a broad range of circumstances where it is not optimal, and for which the optimal detector is not linear. In these cases, nonlinear detectors are theoretically superior, but direct estimation of nonlinear detectors in high-dimensional spaces often leads to flagrant overfitting and poor out-of-sample performance. Despite this difficulty in the general case, we will describe several situations in which nonlinearity can be effectively combined with the AMF to detect weak signals. This allows improvement over AMF performance while avoiding the full force of dimensionality's curse.
When a matched filter is used for detecting a weak target in a cluttered background (such as a gaseous plume in a hyperspectral image), it is important that the background clutter be well-characterized. A statistical characterization can be obtained from the off-plume pixels of a hyperspectral image, but if on-plume pixels are inadvertently included, then that background characterization will be contaminated. In broad area search scenarios, where detection is the central aim, it is by definition unknown which pixels in the scene are off-plume, so some contamination is inevitable. In general, the contaminated background degrades the ability of the matched-filter to detect that signal. This could be a practical problem in plume detection. A linear analysis suggests that the effect is limited, and actually vanishes in some cases. In this study, we take into account the Beer's Law nonlinearity of plume absorption, and we investigate the effect of that nonlinearity on the signal contamination.
To detect weak signals on cluttered backgrounds in high dimensional spaces (such as gaseous plumes in hyperspectral imagery) without excessive false alarms requires that the background clutter be effectively characterized. If the clutter is Gaussian, the well-known linear matched filter optimizes the sensitivity to a given plume signal while suppressing the effect of the background clutter. In practice, the background clutter is rarely Gaussian. Here we illustrate non-linear corrections to the matched filter that are optimal for two non-Gaussian clutter models and we report on parametric and nonparametric characterizations of background clutter.
Independent Component Analysis can be used to analyze cluttered scenes from remote sensing imagery and to detect objects. We show examples in the thermal infrared spectral region (8-12 μm) using both passive hyperspectral data and active multispectral data. The examples are from actual field data and computer simulations. ICA isolates spectrally distinct objects with nearly one-to-one correspondence with the independent component basis functions, making it useful for modeling the clutter in typical scenes. We show examples of chemical plume detection in real and simulated data.
The ultimate performance of any remote sensor is ideally governed by the hardware signal-to-noise capability and allowed signal-averaging time. In real-world scenarios, this may not be realizable and the limiting factors may suggest the need for more advanced capabilities. Moving from passive to active remote sensors offers the advantage of control over the illumination source, the laser. Added capabilities may include polarization discrimination, instantaneous imaging, range resolution, simultaneous multi-spectral measurement, or coherent detection. However, most advanced detection technology has been engineered heavily towards the straightforward passive sensor requirements, measuring an integrated photon flux. The need for focal plane array technology designed specifically for laser sensing has been recognized for some time, but advances have only recently made the engineering possible. This paper will present a few concepts for laser sensing receiver architectures, the driving specifications behind those concepts, and test/modeling results of such designs.
We conducted experiments with side-by-side active and passive sensors in the 8-12 micron region in order to study similarities and differences in the spectral signatures detected by the two sensors. The active instrument was a frequency-agile CO2 lidar system operating on 44 wavelengths and at a total pulse repetition rate of 5 kHz. The passive system was an Aerospace Corp. dispersive imaging spectrometer with 128 spectral channels from 750-1250 cm-l. The sensors viewed both natural scenes and man-made objects typical of industrial scenes at ranges of 1-3 km along horizontal paths. Scenes were viewed under various ambient conditions in order to evaluate the effects of radiance contrast for the passive images at different times of a day. Both imaging and 'staring' experiments were conducted on the background scenes with a significant level of 'clutter'. Preliminary analysis shows that reflectance data (from an active sensor) does not necessarily have a simple relationship to passive data, which is influenced by ground emissivity, atmospheric radiance, and temperature differences.
We report examples of the use of a scanning tunable CO2 laser lidar system in the 9-11 micrometers region to construct images of vegetation and rocks at ranges of up to 5 km from the instrument. Range information is combined with horizontal and vertical distances to yield an image with three spatial dimensions simultaneous with the classification of target type. Object classification is made possible by the distinct spectral signatures of both natural and man-made objects. Several multivariate statistical methods are used to illustrate the degree of discrimination possible among the natural variability of objects in both spectral shape and amplitude.
We report examples of the use of a scanning tunable CO2 laser lidar system in the 9-11 ?m region to construct images of vegetation and rocks at ranges of up to 5 km from the instrument. Range information is combined with horizontal and vertical distances to yield an image with three spatial dimensions simultaneous with the classification of target type. Reflectance spectra in this region are sufficiently distinct to discriminate between several tree species, between trees and scrub vegetation, and between natural and artificial targets. Limitations imposed by laser speckle noise are discussed.
Douglas Nelson, Roger Petrin, Charles Quick, L. John Jolin, Edward MacKerrow, Mark Schmitt, Bernard Foy, Aaron Koskelo, Brian McVey, William Porch, Joseph Tiee, Charles Fite, Frank Archuleta, Michael Whitehead, Donald Walters
The measurement sensitivity of CO2 differential absorption LIDAR (DIAL) can be affected by a number of different processes. Two of these processes are atmospheric optical turbulence and reflective speckle. Atmospheric optical turbulence affects the beam distribution of energy and phase on target. The effects of this phenomenon include beam spreading, beam wander and scintillation which can result in increased shot-to-shot signal noise. In addition, reflective speckle alone has been shown to have a major impact on the sensitivity of CO2 DIAL. We have previously developed a Huygens-Fresnel wave optics propagation code to separately simulate the effects of these two processes. However, in real DIAL systems it is a combination of these phenomena, the interaction of atmospheric optical turbulence and reflective speckle, that influences the results. In this work, we briefly review a description of our model including the limitations along with a brief summary of previous simulations of individual effects. The performance of our modified code with respect to experimental measurements affected by atmospheric optical turbulence and reflective speckle is examined. The results of computer simulations are directly compared with lidar measurements and show good agreement. In addition, simulation studies have been performed to demonstrate the utility and limitations of our model. Examples presented include assessing the effects for different array sizes on model limitations and effects of varying propagation step sizes on intensity enhancements and intensity probability distributions in the receiver plane.
Douglas Nelson, Roger Petrin, Edward MacKerrow, Mark Schmitt, Bernard Foy, Aaron Koskelo, Brian McVey, Charles Quick, William Porch, Joseph Tiee, Charles Fite, Frank Archuleta, Michael Whitehead, Donald Walters
The measurement sensitivity of CO2 differential absorption lidar (DIAL) can be affected by a number of different processes. We have previously developed a Huygens- Fresnel wave optics propagation code to simulate the effects of tow of these processes: effects caused by beam propagation through atmospheric optical turbulence and effects caused by reflective speckle. Atmospheric optical turbulence affects the beam distribution of energy and phase on target. These effects include beam spreading, beam wander and scintillation which can result in increased shot-to-shot signal noise. In addition, reflective speckle alone has been shown to have a major impact on the sensitivity of CO2 DiAL. However, in real DiAL systems it is a combination of these phenomena, the interaction of atmospheric optical turbulence and reflective speckle, that influences the results. The performance of our modified code with respect to experimental measurements affected by atmospheric optical turbulence and reflective speckle is examined. The results of computer simulations are directly compared with lidar measurements. The limitations of our model are also discussed. In addition, studies have been performed to determine the importance of key parameters in the simulation. The result of these studies and their impact on the overall results will be presented.
Charles Quick, Charles Fite, Bernard Foy, L. John Jolin, Aaron Koskelo, Bryan Laubscher, Edward MacKerrow, Brian McVey, Donald Mietz, Douglas Nelson, Robert Nemzek, Roger Petrin, John Quagliano, Patrick Schafstall, Robert Sander, Joseph Tiee, Michael Whitehead
Issues related to the development of direct detection, long- range CO2 DIAL systems for chemical detection and identification are presented and discussed including: data handling and display techniques for large, multi-(lambda) data sets, turbulence effects, slant path propagation, and speckle averaging. Data examples from various field campaigns and CO2 lidar platforms are used to illustrate the issues.
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