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At least 307 records · Page 17
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NASA NTRS2012

Integrating High-Resolution Taskable Imagery into a Sensorweb for Automatic Space-Based Monitoring of Flooding in Thailand

Several space-based assets (Terra, Aqua, Earth Observing One) have been integrated into a sensorweb to monitor flooding in Thailand. In this approach, the Moderate Imaging Spectrometer (MODIS) data from Terra and Aqua is used to perform broad-scale monitoring to track flooding at the regional level (250m/pixel) and EO-1 is autonomously tasked in response to alerts to acquire higher resolution (30m/pixel) Advanced Land Imager (ALI) data. This data is then automatically processed to derive products such as surface water extent and volumetric water estimates. These products are then automatically pushed to organizations in Thailand for use in damage estimation, relief efforts, and damage mitigation. More recently, this sensorweb structure has been used to request imagery, access imagery, and process high-resolution (several m to 30m), targetable asset imagery from commercial assets including Worldview-2, Ikonos, Radarsat-2, Landsat-7, and Geo-Eye-1. We describe the overall sensorweb framework as well as new workflows and products made possible via these extensions.

sensorweb operations↗
NASA NTRS2013

Current and Future Applications of Multispectral (RGB) Satellite Imagery for Weather Analysis and Forecasting Applications

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low ]Earth orbits. The NASA Short ]term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA fs Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channels available from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard METEOSAT ]9. This broader suite includes products that discriminate between air mass types associated with synoptic ]scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES ]R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar ]Orbiting Partnership (S ]NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross ]track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites. New applications from VIIRS extend multispectral composites available from MODIS and SEVIRI while adding new capabilities through incorporation of additional CrIS channels or information from the Near Constant Contrast or gDay ]Night Band h, which provides moonlit reflectance from clouds and detection of fires or city lights. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and recent applications within the operational forecasting environment

Molthan, Andrew L.↗
NASA NTRS2013

International Space Station Instmments Collect Imagery of Natural Disasters

A new focus for utilization of the International Space Station (ISS) is conducting basic and applied research that directly benefits Earth's citizenry. In the Earth Sciences, one such activity is collecting remotely sensed imagery of disaster areas and making those data immediately available through the USGS Hazards Data Distribution System, especially in response to activations of the International Charter for Space and Major Disasters (known informally as the "International Disaster Charter", or IDC). The ISS, together with other NASA orbital sensor assets, responds to IDC activations following notification by the USGS. Most of the activations are due to natural hazard events, including large floods, impacts of tropical systems, major fires, and volcanic eruptions and earthquakes. Through the ISS Program Science Office, we coordinate with ISS instrument teams for image acquisition using several imaging systems. As of 1 August 2013, we have successfully contributed imagery data in support of 14 Disaster Charter Activations, including regions in both Haiti and the east coast of the US impacted by Hurricane Sandy; flooding events in Russia, Mozambique, India, Germany and western Africa; and forest fires in Algeria and Ecuador. ISS‐based sensors contributing data include the Hyperspectral Imager for the Coastal Ocean (HICO), the ISERV (ISS SERVIR Environmental Research and Visualization System) Pathfinder camera mounted in the US Window Observational Research Facility (WORF), the ISS Agricultural Camera (ISSAC), formerly operating from the WORF, and high resolution handheld camera photography collected by crew members (Crew Earth Observations). When orbital parameters and operations support data collection, ISS‐based imagery adds to the resources available to disaster response teams and contributes to the publicdomain record of these events for later analyses.

Evans, C. A.↗
NASA NTRS2014

A Comparison of the Red Green Blue Air Mass Imagery and Hyperspectral Infrared Retrieved Profiles

The Red Green Blue (RGB) Air Mass imagery is derived from multiple channels or paired channel differences. Multiple channel products typically provide additional information than a single channel can provide alone. The RGB Air Mass imagery simplifies the interpretation of temperature and moisture characteristics of air masses surrounding synoptic and mesoscale features. Despite the ease of interpretation of multiple channel products, the combination of channels and channel differences means the resulting product does not represent a quantity or physical parameter such as brightness temperature in conventional single channel satellite imagery. Without a specific quantity to reference, forecasters are often confused as to what RGB products represent. Hyperspectral infrared retrieved profiles of temperature, moisture, and ozone can provide insight about the air mass represented on the RGB Air Mass product and provide confidence in the product and representation of air masses despite the lack of a quantity to reference for interpretation. This study focuses on RGB Air Mass analysis of Hurricane Sandy as it moved north along the U.S. East Coast, while transitioning to a hybrid extratropical storm. Soundings and total column ozone retrievals were analyzed using data from the Cross-track Infrared and Advanced Technology Microwave Sounder Suite (CrIMSS) on the Suomi National Polar Orbiting Partnership satellite and the Atmospheric Infrared Sounder (AIRS) on the National Aeronautics and Space Administration Aqua satellite along with dropsondes that were collected from National Oceanic and Atmospheric Administration and Air Force research aircraft. By comparing these datasets to the RGB Air Mass, it is possible to capture quantitative information that could help in analyzing the synoptic environment enough to diagnose the onset of extratropical transition. This was done by identifying any stratospheric air intrusions (SAIs) that existed in the vicinity of Sandy as the wind field expanded and the cloud pattern evolved into an atypical pattern.

Berndt, E. B.↗
NASA NTRS2014

NASA's Astromaterials Database: Enabling Research Through Increased Access to Sample Data, Metadata and Imagery

The Astromaterials Acquisition & Curation Office at NASA's Johnson Space Center (JSC) is the designated facility for curating all of NASA's extraterrestrial samples. Today, the suite of collections includes the lunar samples from the Apollo missions, cosmic dust particles falling into the Earth's atmosphere, meteorites collected in Antarctica, comet and interstellar dust particles from the Stardust mission, asteroid particles from Japan's Hayabusa mission, solar wind atoms collected during the Genesis mission, and space‐exposed hardware from several missions. To support planetary science research on these samples, JSC's Astromaterials Curation Office hosts NASA's Astromaterials Curation digital repository and data access portal [http://curator.jsc.nasa.gov/], providing descriptions of the missions and collections, and critical information about each individual sample. Our office is designing and implementing several informatics initiatives to better serve the planetary research community. First, we are re‐hosting the basic database framework by consolidating legacy databases for individual collections and providing a uniform access point for information (descriptions, imagery, classification) on all of our samples. Second, we continue to upgrade and host digital compendia that summarize and highlight published findings on the samples (e.g., lunar samples, meteorites from Mars). We host high resolution imagery of samples as it becomes available, including newly scanned images of historical prints from the Apollo missions. Finally we are creating plans to collect and provide new data, including 3D imagery, point cloud data, micro CT data, and external links to other data sets on selected samples. Together, these individual efforts will provide unprecedented digital access to NASA's Astromaterials, enabling preservation of the samples through more specific and targeted requests, and supporting new planetary science research and collaborations on the samples.

Evans, Cindy↗
NASA NTRS2014

The Use of Red Green Blue Air Mass Imagery to Investigate the Role of Stratospheric Air in a Non-Convective Wind Event

The investigation of non-convective winds associated with passing extratropical cyclones and the formation of the sting jet in North Atlantic cyclones that impact Europe has been gaining interest. Sting jet research has been limited to North Atlantic cyclones that impact Europe because it is known to occur in Shapiro-Keyser cyclones and theory suggests it does not occur in Norwegian type cyclones. The global distribution of sting jet cyclones is unknown and questions remain as to whether cyclones with Shapiro-Keyser characteristics that impact the United States develop features similar to the sting jet. Therefore unique National Aeronautics and Space Administration (NASA) products were used to analyze an event that impacted the Northeast United States on 09 February 2013. Moderate Resolution Imaging Spectroradiometer (MODIS) Red Green Blue (RGB) Air Mass imagery and Atmospheric Infrared Sounder (AIRS) ozone data were used in conjunction with NASA's global Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis and higher-resolution regional 13-km Rapid Refresh (RAP) data to analyze the role of stratospheric air in producing high winds. The RGB Air Mass imagery and a new AIRS ozone anomaly product were used to confirm the presence of stratospheric air. Plan view and cross sectional plots of wind, potential vorticity, relative humidity, omega, and frontogenesis were used to analyze the relationship between stratospheric air and high surface winds during the event. Additionally, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to plot trajectories to determine the role of the conveyor belts in producing the high winds. Analyses of new satellite products, such as the RGB Air Mass imagery, show the utility of future GOES-R products in forecasting non-convective wind events.

Berndt, E. B.↗
NASA NTRS2014

Stray Light Artifacts in Imagery from the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) has been collecting imagery of the Earth since its launch aboard Landsat 8 in early 2013. In many respects, TIRS has been exceeding its performance requirements on orbit, particularly in terms of noise and stability. However, several artifacts have been observed in the TIRS data which include banding and absolute calibration discrepancies that violate requirements in some scenes. Banding is undesired structure that appears within and between the focal plane array assemblies. In addition, in situ measurements have shown an error in the TIRS absolute radiometric calibration that appears to vary with season and location within the image. The source of these artifacts has been determined to be out-of-field radiance that scatters onto the detectors thereby adding a non-uniform signal across the field-of-view. The magnitude of this extra signal can be approximately 8% or higher (band 11) and is generally twice as large in band 11 as it is in band 10. A series of lunar scans were obtained to gather information on the source of this out-of-field radiance. Analyses of these scans have produced a preliminary map of stray light, or ghost, source locations in the TIRS out-of-field area. This dataset has been used to produce a synthetic TIRS scene that closely reproduces the banding effects seen in actual TIRS imagery. Now that the cause of the banding has been determined, a stray light optics model is in development that will pin-point the cause of the stray light source. Several methods are also being explored to correct for the banding and the absolute calibration error in TIRS imagery

Stray light↗
NASA NTRS2015

A Bispectral Composite Threshold Approach for Automatic Cloud Detection in VIIRS Imagery

The detection of clouds in satellite imagery has a number of important applications in weather and climate studies. The presence of clouds can alter the energy budget of the Earth‐atmosphere system through scattering and absorption of shortwave radiation and the absorption and re‐emission of infrared radiation at longer wavelengths. The scattering and absorption characteristics of clouds vary with the microphysical properties of clouds, hence the cloud type. Thus, detecting the presence of clouds over a region in satellite imagery is important in order to derive atmospheric or surface parameters that give insight into weather and climate processes. For many applications however, clouds are a contaminant whose presence interferes with retrieving atmosphere or surface information. In these cases, is important to isolate cloud‐free pixels, used to retrieve atmospheric thermodynamic information or surface geophysical parameters, from cloudy ones. This abstract describes an application of a two‐channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels from VIIRS in two spectral tests; a straight‐forward infrared threshold test with the longwave channel and a shortwave - longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The paper and subsequent presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.

LaFontaine Frank J.↗
NASA NTRS2015

Agency Video, Audio and Imagery Library

The purpose of this presentation was to inform the ISS International Partners of the new NASA Agency Video, Audio and Imagery Library (AVAIL) website. AVAIL is a new resource for the public to search for and download NASA-related imagery, and is not intended to replace the current process by which the International Partners receive their Space Station imagery products.

Grubbs, Rodney↗
NASA NTRS2016

Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites

Red-Green-Blue (RGB) composites (EUMETSAT User Services 2009) combine information from several channels into a single composite image. RGB composites contain the same information as the original channels, but presents the information in a more efficient manner. However, RGB composites derived from infrared imagery of both polar-orbiting and geostationary sensors are adversely affected by the limb effect, which interferes with the qualitative interpretation of RGB composites at large viewing zenith angles. The limb effect, or limb-cooling, is a result of an increase in optical path length of the absorbing atmosphere as viewing zenith angle increases (Goldberg et al. 2001; Joyce et al. 2001; Liu and Weng 2007). As a result, greater atmospheric absorption occurs at the limb, causing the sensor to observe anomalously cooler brightness temperatures. Figure 1 illustrates this effect. In general, limb-cooling results in a 4-11 K decrease in measured brightness temperature (Liu and Weng 2007) depending on the infrared band. For example, water vapor and ozone absorption channels display much larger limb-cooling than infrared window channels. Consequently, RGB composites created from infrared imagery not corrected for limb effects can only be reliably interpreted close to nadir, which reduces the spatial coverage of the available imagery. Elmer (2015) developed a reliable, operational limb correction technique for clear regions. However, many RGB composites are intended to be used and interpreted in cloudy regions, so a limb correction methodology valid for both clear and cloudy regions is needed. This paper presents a limb correction technique valid for both clear and cloudy regions, which is described in Section 2. Section 3 presents several RGB case studies demonstrating the improved functionality of limb-corrected RGBs in both clear and cloudy regions, and Section 4 summarizes and presents the key conclusions of this work.

RGB↗
NASA NTRS2016

Applications and Innovations for Use of High Definition and High Resolution Digital Motion Imagery in Space Operations

The first live High Definition Television (HDTV) from a spacecraft was in November, 2006, nearly ten years before the 2016 SpaceOps Conference. Much has changed since then. Now, live HDTV from the International Space Station (ISS) is routine. HDTV cameras stream live video views of the Earth from the exterior of the ISS every day on UStream, and HDTV has even flown around the Moon on a Japanese Space Agency spacecraft. A great deal has been learned about the operations applicability of HDTV and high resolution imagery since that first live broadcast. This paper will discuss the current state of real-time and file based HDTV and higher resolution video for space operations. A potential roadmap will be provided for further development and innovations of high-resolution digital motion imagery, including gaps in technology enablers, especially for deep space and unmanned missions. Specific topics to be covered in the paper will include: An update on radiation tolerance and performance of various camera types and sensors and ramifications on the future applicability of these types of cameras for space operations; Practical experience with downlinking very large imagery files with breaks in link coverage; Ramifications of larger camera resolutions like Ultra-High Definition, 6,000 [pixels] and 8,000 [pixels] in space applications; Enabling technologies such as the High Efficiency Video Codec, Bundle Streaming Delay Tolerant Networking, Optical Communications and Bayer Pattern Sensors and other similar innovations; Likely future operations scenarios for deep space missions with extreme latency and intermittent communications links.

Grubbs, Rodney↗
NASA NTRS2016

Processing Near-Infrared Imagery of the Orion Heatshield During EFT-1 Hypersonic Reentry

The Scientifically Calibrated In-Flight Imagery (SCIFLI) team captured high-resolution, calibrated, near-infrared imagery of the Orion capsule during atmospheric reentry of the EFT-1 mission. A US Navy NP-3D aircraft equipped with a multi-band optical sensor package, referred to as Cast Glance, acquired imagery of the Orion capsule's heatshield during a period when Orion was slowing from approximately Mach 10 to Mach 7. The line-of-sight distance ranged from approximately 65 to 40 nmi. Global surface temperatures of the capsule's thermal heatshield derived from the near-infrared intensity measurements complemented the in-depth (embedded) thermocouple measurements. Moreover, these derived surface temperatures are essential to the assessment of the thermocouples' reliance on inverse heat transfer methods and material response codes to infer the surface temperature from the in-depth measurements. The paper describes the image processing challenges associated with a manually-tracked, high-angular rate air-to-air observation. Issues included management of significant frame-to-frame motions due to both tracking jerk and jitter as well as distortions due to atmospheric effects. Corrections for changing sky backgrounds (including some cirrus clouds), atmospheric attenuation, and target orientations and ranges also had to be made. The image processing goal is to reduce the detrimental effects due to motion (both sensor and capsule), vibration (jitter), and atmospherics for image quality improvement, without compromising the quantitative integrity of the data, especially local intensity (temperature) variations. The paper will detail the approach of selecting and utilizing only the highest quality images, registering several co-temporal image frames to a single image frame to the extent frame-to-frame distortions would allow, and then co-adding the registered frames to improve image quality and reduce noise. Using preflight calibration data, the registered and averaged infrared intensity images were converted to surface temperatures on the Orion capsule's heatshield. Temperature uncertainties will be discussed relative to uncertainties of surface emissivity and atmospheric transmission loss. Comparison of limited onboard surface thermocouple data to the image derived surface temperature will be presented.

Spisz, Thomas S.↗
NASA NTRS2016

Client-Side Data Processing and Training for Multispectral Imagery Applications in the GOES-R Era

RGB imagery can be created locally (i.e. client-side) from single band imagery already on the system with little impact given recommended change to texture cache in AWIPS II. Training/Reference material accessible to forecasters within their operational display system improves RGB interpretation and application as demonstrated at OPG. Application examples from experienced forecasters are needed to support the larger community use of RGB imagery and these can be integrated into the user's display system.

VIIRS↗
NASA NTRS2017

Detection of Hail Storms in Radar Imagery Using Deep Learning

In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.

natural hazard↗
NASA NTRS2018

Image Station Matching, Preprocessing, Spatial Registration and Change Detection with Multi-Temporal Remotely-Sensed Imagery

A method for collecting and processing remotely sensed imagery in order to achieve precise spatial co-registration (e.g., matched alignment) between multi-temporal image sets is presented. Such precise alignment or spatial co-registration of imagery can be used for change detection, image fusion, and temporal analysis/modeling. Further, images collected in this manner may be further processed in such a way that image frames or line arrays from corresponding photo stations are matched, co-aligned and if desired merged into a single image and/or subjected to the same processing sequence. A second methodology for automated detection of moving objects within a scene using a time series of remotely sensed imagery is also presented. Specialized image collection and preprocessing procedures are utilized to obtain precise spatial co-registration (image registration) between multitemporal image frame sets. In addition, specialized change detection techniques are employed in order to automate the detection of moving objects.

Coulter, Lloyd Lawrence Arthur↗
NASA NTRSDate not supplied

Utilizing Airborne and Space-Based Remote Sensing Imagery to Implement the Unvegetated-Vegetated Ratio to Assess Salt Marsh Vulnerability in South Carolina

Among the most productive ecosystems on earth, salt marshes provide crucial ecosystem services including water filtration, shoreline protection, storm surge buffering, and flood mitigation. Marshes are largely dependent on their sediment budget which can significantly vary across a region and can be used to determine the life span of the marsh. Upstream land use change near Charleston, South Carolina, along with rising sea levels, are expected to alter sediment budgets and threaten marsh stability and long-term health. The unvegetated-vegetated ratio (UVVR), developed by researchers at USGS, is a scalable and efficient method to assess vulnerability. The NASA DEVELOP National Program collaborated with the South Carolina Department of Natural Resources, the South Carolina Department of Health and Environmental Control, and the United States Geological Survey Woods Hole Coastal and Marine Science Center to apply the UVVR method within Google Earth Engine. Marsh vulnerability was analyzed using UVVR derived from clustering and manual interpretation of National Agriculture Imagery Program (NAIP) high-resolution aerial imagery. NAIP derived UVVR was aggregated to Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) resolution and projection. A Random Forest Regression between Landsat derived data and UVVR was modeled to estimate a potential relationship. The estimation of this relationship was used to produce temporal change analysis maps of salt marsh vulnerability back to 1984. The NAIP imagery processed through Google Earth Engine allowed us to make detailed UVVR maps for 2009, 2015, 2017, and 2019 for decision making within South Carolina. Google Earth Engine scripting provided a novel approach to UVVR methodology that will allow decision makers to input new marsh regions and easily calculate marsh vulnerability without external data downloading. These results were used to understand what areas of the marsh need most resource allocation in the future.

NASA DEVELOP↗
NASA NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗
NASA NTRSDate not supplied

X-Ray Imagery as the Record of All Data of Interest in Hypervelocity Impact Fragment Studies

Laboratory study of hypervelocity spacecraft fragmentation has traditionally involved the collection and analysis of fragments that were caught in deceleration material surrounding the impact. This process has typically involved the disintegration of the catchment material either through chemical dissolution, or through physical excavation to recover the fragments. Due to the scale of the three impact tests—the Satellite Orbital Debris Characterization Impact Test (SOCIT), the DebriSat satellite impact test, and the DebrisLV launch vehicle impact test—the latter two using more than 12 cubic meters of polyurethane foam to capture the fragments, hese projects have used x-ray imagery to precisely locate and thus, to more efficiently extract fragments in the soft-catch material. Three years into the DebriSat fragment extraction process, a side study was initiated to explore what additional information could be discerned from the x-rays, with significant results. This study was instrumental to a rapid replacement and retooling as the project was forced to replace the x-ray system around which the extraction process had been based. The revised process continues to map the debris for extraction. The project has, in parallel, systematically addressed the limits/tolerances of what x-rays can reveal about size, shape, density, mass, velocity, energy, and deformation/damage of the fragment during the deceleration in the catchment material while replicating the original extraction mapping function. All of these features have been optimized or have sufficient understanding to characterize the basic factors that will define a complete data set extracted solely from x-ray imagery. It is an ideal time to develop such a process, with extracted fragments providing “ground truth” against image-only data, and abundant available imagery of the same fragments under both the prior and replacement x-ray technologies, which have several fundamentally different characteristics. This paper addresses the types and quality of hypervelocity fragmentation data that can be and has been extracted from x-rays. It further addresses the question of whether and under what circumstances future hypervelocity experiments can use x-ray methods to largely—or to completely—avoid the extraction process in recording all appropriate results. Lastly, this paper addresses lessons learned and how future efforts can be further optimized.

John B. Bacon↗