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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.

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NASA NTRS2018

The New CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.

Kiely, Aaron B.↗
NASA NTRS2018

Analysis and Applications of Water Vapor-Derived Multispectral Composites for Geostationary Satellites

Analysis of multispectral (red-green-blue, RGB) satellite image composites can be used to improve understanding of thermodynamic and / or dynamic features associated with the development of significant weather events (cyclones, hurricanes, intense convection, turbulence, etc.). These composites minimize the need for a more time consuming analysis of multiple images and can provide additional insight not available from a single channel. The enhanced water vapor imaging capabilities of the Advanced Baseline Imager on GOES-16,-17 satellites provide a unique opportunity to demonstrate this capability through a comparison of the Air Mass and Differential Water Vapor RGB image products for several case studies.

Jedlovec, Gary J.↗
NASA NTRS2019

Analysis and Applications of Water Vapor-Derived Multispectral Composites for Geostationary Satellites

Analysis of multispectral (red-green-blue, RGB) satellite image composites can be used to improve understanding of thermodynamic and / or dynamic features associated with the development of significant weather events (cyclones, hurricanes, intense convection, turbulence, etc.) The enhanced water vapor imaging capabilities of the Advanced Baseline Imager on GOES-16 and GOES-17 satellites provide a unique opportunity to demonstrate this capability through a comparison of the Air Mass (AM) and Differential Water Vapor (DWV) RGB image products for several case studies.

Berndt, Emily↗
NASA NTRS2019

Rock Hard Science: Multispectral and Mineralogical Investigations to Understand Bedrock Spectral Properties and Strength at Vera Rubin Ridge, Gale Crater, Mars

Since the beginning of the Mars Science Laboratory (MSL) mission, Vera Rubin Ridge (VRR) has been a location of interest to the MSL science team because of its apparent erosional resistance and strong near-IR (~860 nm) absorption feature seen from orbit in the Mars Reconnaissance Orbiter mission's Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data. The strong CRISM absorption feature along VRR was hypothesized to be primarily associated with an increased abundance of crystal-line hematite compared to lower Mt. Sharp units. How-ever, surface multispectral and mineralogic data, from the Mastcam and CheMin instruments onboard the Curiosity rover, suggest hematite is not the only mineral contributing to the near-IR absorption feature measured in VRR or the reason for its relative hardness.

Jacob, S. R.↗
NASA NTRS2019

Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data

Applications in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on spatial and temporal resolutions of satellite observations. However, there are typically trade-offs between spatial and temporal resolutions in dataset selection. For instance, geostationary weather tracking satellites are designed to take snapshots many times throughout the day but sensor hardware limits data collection. In this work we tackle this limitation, developing a method for temporal upsampling of multi-spectral satellite imagery using optical flow video interpolation deep convolutional neural networks. The presented model, extends Super SloMo (SSM) from single optical flow estimates to multichannel where flows are computed per band. We apply this technique on 8 multi-spectral bands of NOAA/NASA's GOES-16 mesoscale dataset to temporally enhance full disk hemispheric snapshots from 15 minutes to 1 minute. Through extensive experimentation, we show SSM vastly outperforms the linear interpolation baseline and that multichannel optical flows improves performance on GOES-16. A visual analysis of optical flow vectors clearly identifies hurricanes and large-scale atmospheric dynamics. Furthermore, we discuss challenges and open questions related to optical flow and temporal interpolation of multispectral geostationary satellite imagery.

Optical Flow↗
NASA NTRS2019

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

Robert Campbell↗
NASA NTRS2019

Tunable mid-wave infrared spectral filters based on GexSbyTez for multispectral imaging

The mid-wave infrared (MWIR) spectrum contains a wealth of invaluable information including the spectral ‘fingerprint’ of many chemical species and has applications in remote sensing and astronomical imaging. Traditionally, filtering for the MWIR is achieved by means of passive multilayer interference (dichroic) filters, Fabry-Perot-based micro-electro-mechanical system (MEMS) filters, liquid crystal tunable filters, and focal plane array (FPA) filters. For accurate multispectral imaging applications, these approaches suffer from various limitations such as: having moving parts; exhibiting slow response times, and; having limited spectral bandwidth / resolution. Recently, there has been significant interest toward ‘active’ spectral imaging technologies, whereby the ability to provide electrically tunable narrowband filtering—spanning the entire MWIR—is highly desirable. In this work we introduce a new spectral imaging technology, namely actively tunable optical transmission filters using the phase-change material (PCM) GexSbyTez (GST). The GST exhibits a large, reversible change in its refractive index across the MWIR upon a phase transition (from amorphous to crystalline). This refractive index modulation governs the filter’s optical characteristics. Through an optical stimulus or applied voltage—which changes the GST state from amorphous to crystalline—one can actively tune the filter with MHz speed. We incorporate GST into optimized guided mode resonance (GMR) and plasmonic nanohole array (PNA) device architectures, enabling <10nm spectral resolution and tunable operation across 3~5µm. Our proposed PCM-MWIR filter will be able to extract the maximum amount of ‘useful’ information within the atmosphere for remote Earth sensing measurements at operational speeds orders of magnitude faster than current airborne-based sensors. Moreover, it may enable affordable SmallSat-based MWIR instrumentation which is complimentary to other observation systems. The MISSE (Materials International Space Station Experiment-Flight Facility) experiment has been proposed as a testbed for a PCM-based tunable MWIR filter module to allow exposure of the module to the low Earth orbit space environment. This will provide valuable data regarding the robustness of the filter to withstand the radiation and atomic oxygen environment and allow assessment of the technology for use in space applications.

Kim, Hyun Jung↗
NASA NTRS2020

Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering absorption, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard MODIS/VIIRS continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR [Kou et al., 1993] and one in the MWIR [Wagner et al., 2005]. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1-2 μm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature results in more consistent retrievals between the two spectrally distinct 2.2 μm atmospheric window channels on MODIS and VIIRS. As a result, the 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

MODIS↗
NASA NTRSDate not supplied

Comparison of Multispectral Imaging and Traditional Fundoscopy in the Detection of Terrestrial Retinal and Optic Nerve Pathologies like those Encountered During and/or Immediately Following Long-Duration Spaceflight

INTRODUCTION: The purpose of this investigation was to evaluate if MultiColor Imaging (MCI) can replace color fundus photography (CFP) as a diagnostic screening tool during spaceflight. MCI significantly reduces crew time (approx. 115 minutes/session, 36 hours/year) by eliminating nominal on-orbit fundoscopy sessions, while also providing the option to capture a larger field of view (55 vs. 35). METHODS: A comprehensive PubMed literature search was conducted using the following key words: multicolor, multispectral, imaging, retina, choroid, optic nerve, optic disc, and papilledema. Publications were filtered based on optic nerve and chorioretinal pathologies matching those seen during or immediately after spaceflight: optic disc edema (ODE), cotton wool spots (CWS), retinal hemorrhage, pigment epithelial detachment (PED), and serous chorioretinopathy (SCR). In a separate effort, 44 multicolor images (30 abnormal) of terrestrial patients were graded and compared to corresponding color fundus images acquired at the Doheny Eye Centers and UCLA. RESULTS: The search identified 340 articles; 9 describing MCI in relevant pathologies, 6 comparing MCI to CFP. MCI is superior in detecting CWS (1 paper), PED (2 papers), retinal hemorrhages (2 papers), and choroidal folds (1 paper), and can better delineate extent or boundaries of subretinal fluid and identify areas of RPE damage in SCR (2 papers). On MCI, ODE was described as a hyperreflective ring with a green shift and indistinct disc margins, with equaldetectability as using CFP (3 papers). Grading at Doheny Eye Institute confirmed these findings. DISCUSSION: MCI can effectively detect all retinal and optic nerve findings detectable by CFP during and immediately post-spaceflight and represents a suitable replacement as an on-orbit diagnostic screening tool. Additionally, by eliminating the nominal on-orbit fundoscopy sessions, dozens of crew hours are spared per year by utilizing MCI.

Jorge Nagel↗
NASA NTRS2020

Snow Property Inversion from Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach with Examples from MODIS and Landsat 8 OLI

Spectral mixture analysis has a history in mappingsnow, especially where mixed pixels prevail. Using multiplespectral bands rather than band ratios or band indices, retrievalsof snow properties that affect its albedo lead to more accu-rate estimates than widely used age-based models of albedoevolution. Nevertheless, there is substantial room for improve-ment. We present the Snow Property Inversion from RemoteSensing (SPIReS) approach, offering the following improve-ments: 1) Solutions for grain size and concentrations of lightabsorbing particles are computed simultaneously; 2) Only snowand snow-free endmembers are employed; 3) Cloud-maskingand smoothing are integrated; 4) Similar spectra are groupedtogether and interpolants are used to reduce computation time.The source codes are available in an open repository. Com-putation is fast enough that users can process imagery ondemand. Validation of retrievals from Landsat 8 operational landimager (OLI) and moderate-resolution imaging spectroradiome-ter (MODIS) against WorldView-2/3 and the Airborne SnowObservatory shows accurate detection of snow and estimatesof fractional snow cover. Validation of albedo shows low errorsusing terrain-correctedin situmeasurements. We conclude bydiscussing the applicability of this approach to any airborne orspaceborne multispectral sensor and options to further improve retrievals.

Edward H Blair↗
NASA NTRSDate not supplied

Mars Science Laboratory Mastcam Multispectral Investigation of Drill Targets From the Beginning of the Clay Sulfate Transition to Marker Band Valley

In March, 2021 the Mars Science Laboratory Curiosity rover officially started exploring the Clay Sulfate Transition (CST). This area of Mt. Sharp has been interpreted to have hydrated Mg-sulfate spectral signatures in CRISM orbital data, and it no longer has the strong phyllosilicate spectral signature as the previous Glen Torridon region. Recently, the Curiosity rover has also encountered the marker band that was also identified in orbital data [1,3]. This shift from phyllosilicates to hydrated Mg-sulfates suggests a major change in the environmental conditions during which these rocks were deposited. This abstract focuses on changes seen in the Mastcam multispectral data between the CST drill targets.

S R Jacob↗
NASA NTRS2022

Geological, Multispectral, and Meteorological Imaging Results from the Mars 2020 Perseverance Rover in Jezero Crater

Perseverance’s Mastcam-Z instrument provides high-resolution stereo and multispectral images with a unique combination of spatial resolution, spatial coverage, and wavelength coverage along the rover’s traverse in Jezero crater, Mars. Images reveal rocks consistent with an igneous (including volcanic and/or volcaniclastic) and/or impactite origin and limited aqueous alteration, including polygonally fractured rocks with weathered coatings; massive boulder-forming bedrock consisting of mafic silicates, ferric oxides, and/or iron-bearing alteration minerals; and coarsely layered outcrops dominated by olivine. Pyroxene dominates the iron-bearing mineralogy in the fine-grained regolith, while olivine dominates the coarse-grained regolith. Solar and atmospheric imaging observations show significant intra- and intersol variations in dust optical depth and water ice clouds, as well as unique examples of boundary layer vortex action from both natural (dust devil) and Ingenuity helicopter–induced dust lifting. High-resolution stereo imaging also provides geologic context for rover operations, other instrument observations, and sample selection, characterization, and confirmation.

Mars 2020↗
NASA NTRS2011

Review and Implementation of the Emerging CCSDS Recommended Standard for Multispectral and Hyperspectral Lossless Image Coding

A new standard for image coding is being developed by the MHDC working group of the CCSDS, targeting onboard compression of multi- and hyper-spectral imagery captured by aircraft and satellites. The proposed standard is based on the "Fast Lossless" adaptive linear predictive compressor, and is adapted to better overcome issues of onboard scenarios. In this paper, we present a review of the state of the art in this field, and provide an experimental comparison of the coding performance of the emerging standard in relation to other state-of-the-art coding techniques. Our own independent implementation of the MHDC Recommended Standard, as well as of some of the other techniques, has been used to provide extensive results over the vast corpus of test images from the CCSDS-MHDC.

hyperspectral images↗