Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hyperspectral imaging”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24

EO-1/Hyperion: Nearing Twelve Years of Successful Mission Science Operation and Future Plans

The Earth Observing One (EO-1) satellite is a technology demonstration mission that was launched in November 2000, and by July 2012 will have successfully completed almost 12 years of high spatial resolution (30 m) imaging operations from a low Earth orbit. EO-1 has two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for NASA's newer satellite missions, including the forthcoming (in early 2013) Landsat-8 and the future Hyperspectral Infrared Imager (HyspIRI). As well, EO-1 is a heritage platform for the upcoming German satellite, EnMAP (2015). Here, we provide an overview of the mission, and highlight the capabilities of the Hyperion for support of science investigations, and present prototype products developed with Hyperion imagery for the HyspIRI and other space-borne spectrometers.

Middleton, Elizabeth M.↗

Smart Crop Farming Systems for Artemis Exploration Missions

Space crop production systems that mitigate risks of crew poor performance or illness due to inadequate food and nutrition are needed during manned Artemis exploration missions beyond LEO. Prototype farms must be designed for deployment on ISS and tested in manned platforms: Gateway, lunar habitats, and Mars trans-hab spacecraft in preparation for human missions to Mars. Food production must be optimal and safe for human consumption. Thus, plant growth facilities (i.e. Veggie and APH) can be enhanced with imaging systems (including hyperspectral, multispectral, lidar, and fluorescence imaging systems) for nondestructive monitoring of plant health, stress and assessing food safety. Databases of crop responses to stress obtained during ground studies can be used to develop novel artificial intelligence (AI) algorithms for optimizing crop production (i.e. environmental settings during growth) and for detecting crop indices that ensure food safety. Future farming systems should be sustainable and smart. Novel adaptive AI algorithms requiring limited data sets for calibration are needed for reducing crew intervention during plant cultivation except for maintenance and harvesting events. Eventually, AI driven control systems that include autonomous planting, growing, and harvesting as well as periodic sanitization need evaluation for supplementing crew diets with fresh produce during future Mars exploration missions.

O Monje↗

Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Multispectral imaging system for contaminant detection

An automated inspection system for detecting digestive contaminants on food items as they are being processed for consumption includes a conveyor for transporting the food items, a light sealed enclosure which surrounds a portion of the conveyor, with a light source and a multispectral or hyperspectral digital imaging camera disposed within the enclosure. Operation of the conveyor, light source and camera are controlled by a central computer unit. Light reflected by the food items within the enclosure is detected in predetermined wavelength bands, and detected intensity values are analyzed to detect the presence of digestive contamination.

Poole, Gavin H.↗

Development and Airborne Demonstration of the Concurrent Artificially-Intelligent Spectrometry and Adaptive Lidar System: Advancing Lidar Capabilities for the STV Observing System

We report on the design, build and planned airborne demonstration of a spaceflight-prototype Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS). The CASALS lidar is an Adaptive Wavelength Scanning Lidar (AWSL) operating in push broom mode. The demonstration has three major goals: advance the Technical Readiness Level of the AWSL hardware, validate its measurement performance and mature algorithms and methods needed for the Surface Topography and Vegetation (STV) observing system. AWSL acquires parallel tracks of surface heights by rapidly steering a laser beam across a swath. A 1040nm-centered laser is tuned across 30nm and carved into 2-ns pulses, the pulse energy is fiber amplified and the pulses are dispersed cross-track using a non-mechanical wavelength-to-angle grating. For the spaceflight system the beam will be pointable to 1200 10m footprints across a 7km swath. For the airborne demonstration there will be 256 0.7m footprints across a 110m swath. In both cases the footprints overlap across- and along-track for uniform target illumination. For the airborne demonstration a steering mirror will increase the accessible swath width to 4km. At the receiver, solar radiation is filtered with a narrow-slit grating-spectrometer and the footprints are imaged onto a linear-mode, photon-sensitive HgCdTe APD-array. The received pulses are time-division-multiplexed to a few high-speed analog-to-digital converters to record waveforms. Spaceflight and airborne CASALS are designed to nominally detect 20 photons per pulse and, by averaging 27 overlapping footprints, achieve 2cm flat target range precision and high-quality vegetation structure waveforms The AWSL will be flown in the summer of 2024, along with a Headwall VNIR-SWIR hyperspectral sensor imaging a 4km wide swath, at NEON eddy covariance flux towers in the U.S. mid-Atlantic where high resolution hyperspectral and lidar data, acquired annually, are available for validation.

Guangning Yang↗

Spatially dependent H-bond dynamics at interfaces of water/biomimetic self-assembled lattice materials

Significance It is challenging to develop biomimetic lattice self-assemblies because such materials need to resemble the flexibility and crystallinity of their biological analog. We show that these materials exhibit unique micro- and mesoscopic-ordered ultrafast hydrogen-bond dynamics, which could be a key feature in the lattice self-assembly: the ultrafast dynamics ensure flexibility, whereas the domain-level ordering reflects crystallinity. This scientific insight cannot be revealed without the ultrafast transient vibrational sum-frequency generation microscope, which integrates ultrafast interfacial spectroscopy with interfacial molecular vibrational imaging––a state-of-the-art development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Atmospheric sounding at JPL: current and future technologies

We discuss lessons learned on AIRS in the development and operations as well as plans for next generation systems including SIRAS, a wide field hyperspectral infrared imaging spectrometer which offers AIRS spectral performance at 24x the spatial resolution.

infrared↗

The West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE): A Prototype for A PBL Mission of Missions

We present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment(WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.

Planetary Boundary Layer↗

Mineralogical, Elemental, and Tomographic Reconnaissance Investigation for CLPS (METRIC)

METRIC is a robotic science laboratory that can determine the mineralogy, elemental chemistry, micromorphology, and thermophysical properties of planetary regolith. The METRIC suite comprises METRIC XRD/F, an X-ray diffraction/X-ray fluorescence instrument that can determine the mineralogy and elemental chemistry of regolith samples; METRIC XCT, a micro X-ray computed tomography instrument that can be used to evaluate grain/crystallite sizes and textures; METRIC IRS, an imaging spectrometer mounted on a rover that can determine mineralogy and thermophysical properties at the landing site; and a pneumatic sample collection, processing, distribution system developed by Honeybee Robotics. The payload elements could be deployed on a static lander or a rover. Data returned from the METRIC payload would inform origin, formation, and evolution of rocky planetary bodies. METRIC XRD/F draws on heritage from the CheMin instrument on the Mars Science Laboratory (MSL) Curiosity rover [1], with a few important improvements. Like CheMin, METRIC XRD/F operates in transmission geometry and uses piezoelectric actuators on sample cells in a tuning fork geometry to induce convective grain motion of the regolith to create a randomly oriented powder. MSL CheMin uses an energy-sensitive CCD to collect XRD patterns and XRF spectra simultaneously from the same sample cell, resulting in qualitative XRF data. METRIC XRD/F uses two different sample cells, one optimized for XRD and one optimized for XRF, and a silicon drift detector to detect fluoresced X-rays. This improvement to the XRF capabilities provides quantitative geochemical data of major elements down to Z = 11 and allows for the detection of minor and trace elements that are critical for evaluating geologic evolution of the Moon (e.g., P and Th). Modest improvements to the XRD geometry and hardware allow for better angular resolution and the ability to distinguish between members of the pyroxene group. METRIC XCT uses the same geometry and much of the same hardware as METRIC XRD/F, where a CCD would capture images of a regolith sample in a 3 mm diameter sample tube that is rotated 360° in steps <1°. Image brightness can be used to infer compositional data, where brighter materials indicate a higher Z, much like scanning electron microscopy. Data from METRIC XCT complement those from METRIC XRD/F. Particle size, shape, and texture can provide petrologic and provenance information, whereas vesicle size and morphology in volcanic or impact melt lithologies can inform cooling rates. METRIC IRS is a hyperspectral thermal imager that can be mounted to a lander or rover to provide mineralogical data from the broader landing site and help determine whether the samples analyzed by METRIC XRD/F and XCT are representative. The METRIC IRS spectral range (8–14 μm) and resolution (10.8 cm-1) allow for quantitative mineralogy from modelling Reststrahlen bands of major rock-forming minerals (e.g., silicates, phosphates). Radiance cubes can be processed and modelled with an onboard high-performance computer to determine mineral abundances of plagioclase, high-Ca pyroxene, pigeonite, orthopyroxene, olivine, and glass. Regolith samples can be acquired, processed, and delivered to the X-ray instruments via multiple sample handling systems, but the pneumatic sampling systems developed by Honeybee Robotics [e.g., 2] are best suited for relatively low-cost missions that are being competed for the Moon (e.g., NASA’s Payloads and Research Investigations for the Surface of the Moon program). There are pneumatic sampling systems that collect surface material and other systems that pneumatically drill up to ~1 m below the surface, providing material that has not been space weathered and has not been affected by the lander’s exhaust. [1] Blake, D. F., Vaniman, D., Achilles, C., Anderson, R., Bish, D., et al. (2012). Space Sci. Rev. 170, 341-478. https://doi.org/10.1007/s11214-012-9905-1. [2] Zacny, K., Betts, B., Hedlund, M., Long, P., Gramlich, M., Tura, K., Chu, P., Jacob, A., Garcia, A. (2014). IEEE Aerospace Conference, 3-7 March 2014, Big Sky, MT, U.S.A.

X-ray diffraction↗

Combining variational autoencoders and physical bias for improved microscopy data analysis *

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO 3 , and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data. The customized codes of the required functions and classes to develop phyVAE is available at https://github.com/arpanbiswas52/phy-VAE.

97 MATHEMATICS AND COMPUTING↗

Hyperspectral Thermal Emission Spectrometer: Engineering Flight Campaign

The Hyperspectral Thermal Emission Spectrometer (HyTES) successfully completed its first set of engineering test flights. HyTES was developed in support of the Hyperspectral Infrared Imager (HyspIRI). HyspIRI is one of the Tier II Decadal Survey missions. HyTES currently provides both high spectral resolution (17 nm) and high spatial resolution (2-5m) data in the thermal infrared (7.5-12 micron) part of the electromagnetic spectrum. HyTES data will be used to help determine the optimum band positions for the HyspIRI Thermal Infrared (TIR) sensor and provide antecedent data for HyspIRI related studies.

HyTES↗

Detection of Anomalies in the UV/Vis Reflectances from the Ozone Monitoring Instrument

Various instrumental or geophysical artifacts, such as saturation, stray light, or obstruction of light (either coming from the instrument or related to solar eclipses), negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra and downstream retrievals of atmospheric and surface properties derived from these spectra. In addition, excessive noise such as from cosmic ray impacts, prevalent within the South Atlantic Anomaly, can also degrade satellite radiance measurements. Saturation specifically pertains to observations of very bright surfaces such as sun glint over open water or thick clouds. When saturation occurs, additional photoelectric charge generated at the saturated pixel may overflow to pixels adjacent to a saturated area and be reflected as a distorted image in the final sensor output.When these effects cannot be corrected to an acceptable level for science quality retrievals, flagging of the affected pixels is indicated. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectraover a 10 nm-spectral range; our Decorrelation Index (DI for brevity) is simply defined as DI=1-r. DI increases with anomalous additive effects or excessive noise in either radiances, the most likely cause indata from theOzone Monitoring Instrument (OMI),or irradiances. DI is relatively straight-forward to use and interpret and can be 20computed for different wavelength intervals. We developed a set of DIs for two spectral channelsof the OMI, a hyperspectral pushbroom imaging spectrometer. For each OMI spatial measurement, we define 14 wavelength-dependent DIs within the OMI visible channel (350-498 nm) and 6 DIs in its ultraviolet 2 (UV2) channel (310-370 nm). As defined, DIs reflect a continuous range of deviations of observed spectra from the reference irradiance spectrum that are complementary to the binary Saturation Possibility Warning (SPW) flags currently provided for each individual spectral/spatial pixel in the OMI radiance data set. Smaller values of DI are also caused by a number of geophysical factors; this allows one to obtain interesting physical results on the global distribution of spectral variations.

Space sensor↗

Multidimensional Coherent Spectroscopy of van der Waals materials and heterostructures (Final Technical Report)

This project advanced multidimensional coherent spectroscopy (MDCS) and related spectroscopic-imaging methods as quantitative probes of many-body optical excitations in two-dimensional (2D) van der Waals (vdW) semiconductors and their heterostructures. Monolayer transition-metal dichalcogenides (TMDs) such as MoSe 2 and WSe 2 provide a uniquely strong platform for excitonic physics due to reduced dielectric screening, which enhances Coulomb interactions and makes higher-order correlated states (e.g., biexcitons and exciton–trion correlations) more prominent than in conventional bulk semiconductors. At the same time, the same sensitivity that enables strong interactions also increases susceptibility to spatial inhomogeneity—e.g., strain gradients, wrinkles, nanoscale disorder potentials, and charge puddling—which can broaden resonances, obscure interaction signatures, and complicate device-to-device reproducibility. The work reported here addressed this dual opportunity and challenge by: (i) developing and deploying coherent multidimensional methods (including double-quantum MDCS) capable of isolating interaction signals with reduced background, (ii) integrating electrostatic control to tune carrier populations and interaction strengths in situ, and (iii) introducing complementary spatially resolved photoluminescence (PL) techniques—hyperspectral PL imaging and polarization-/field-dependent PL lineshape analysis—to directly quantify disorder, strain, and localization that govern the coherent response.

36 MATERIALS SCIENCE↗

BCARS Simulated Phantom Dataset for Evaluation of Processing Pipelines

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.

Dixon, Jessica Z. [Georgia Institute of Technology↗

Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. In this study, we discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.

36 MATERIALS SCIENCE↗

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

High-resolution space-based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise ratio. Different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Intrinsic dimensionality↗

Crosscutting Airborne Remote Sensing Technologies for Oil and Gas and Earth Science Applications

Airborne imaging spectroscopy has evolved dramatically since the 1980s as a robust remote sensing technique used to generate 2-dimensional maps of surface properties over large spatial areas. Traditional applications for passive airborne imaging spectroscopy include interrogation of surface composition, such as mapping of vegetation diversity and surface geological composition. Two recent applications are particularly relevant to the needs of both the oil and gas as well as government sectors: quantification of surficial hydrocarbon thickness in aquatic environments and mapping atmospheric greenhouse gas components. These techniques provide valuable capabilities for petroleum seepage in addition to detection and quantification of fugitive emissions. New empirical data that provides insight into the source strength of anthropogenic methane will be reviewed, with particular emphasis on the evolving constraints enabled by new methane remote sensing techniques. Contemporary studies attribute high-strength point sources as significantly contributing to the national methane inventory and underscore the need for high performance remote sensing technologies that provide quantitative leak detection. Imaging sensors that map spatial distributions of methane anomalies provide effective techniques to detect, localize, and quantify fugitive leaks. Airborne remote sensing instruments provide the unique combination of high spatial resolution (<1 m) and large coverage required to directly attribute methane emissions to individual emission sources. This capability cannot currently be achieved using spaceborne sensors. In this study, results from recent NASA remote sensing field experiments focused on point-source leak detection, will be highlighted. This includes existing quantitative capabilities for oil and methane using state-of-the-art airborne remote sensing instruments. While these capabilities are of interest to NASA for assessment of environmental impact and global climate change, industry similarly seeks to detect and localize leaks of both oil and methane across operating fields. In some cases, higher sensitivities desired for upstream and downstream applications can only be provided by new airborne remote sensing instruments tailored specifically for a given application. There exists a unique opportunity for alignment of efforts between commercial and government sectors to advance the next generation of instruments to provide more sensitive leak detection capabilities, including those for quantitative source strength determination.

hyperspectral↗