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All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Monitoring Disasters by Use of Instrumented Robotic Aircraft

Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.

Wegener, Steven S.↗
NASA NTRS2013

Support Routines for In Situ Image Processing

This software consists of a set of application programs that support ground-based image processing for in situ missions. These programs represent a collection of utility routines that perform miscellaneous functions in the context of the ground data system. Each one fulfills some specific need as determined via operational experience. The most unique aspect to these programs is that they are integrated into the large, in situ image processing system via the PIG (Planetary Image Geometry) library. They work directly with space in situ data, understanding the appropriate image meta-data fields and updating them properly. The programs themselves are completely multimission; all mission dependencies are handled by PIG. This suite of programs consists of: (1)marscahv: Generates a linearized, epi-polar aligned image given a stereo pair of images. These images are optimized for 1-D stereo correlations, (2) marscheckcm: Compares the camera model in an image label with one derived via kinematics modeling on the ground, (3) marschkovl: Checks the overlaps between a list of images in order to determine which might be stereo pairs. This is useful for non-traditional stereo images like long-baseline or those from an articulating arm camera, (4) marscoordtrans: Translates mosaic coordinates from one form into another, (5) marsdispcompare: Checks a Left Right stereo disparity image against a Right Left disparity image to ensure they are consistent with each other, (6) marsdispwarp: Takes one image of a stereo pair and warps it through a disparity map to create a synthetic opposite- eye image. For example, a right eye image could be transformed to look like it was taken from the left eye via this program, (7) marsfidfinder: Finds fiducial markers in an image by projecting their approximate location and then using correlation to locate the markers to subpixel accuracy. These fiducial markets are small targets attached to the spacecraft surface. This helps verify, or improve, the pointing of in situ cameras, (8) marsinvrange: Inverse of marsrange . given a range file, re-computes an XYZ file that closely matches the original. . marsproj: Projects an XYZ coordinate through the camera model, and reports the line/sample coordinates of the point in the image, (9) marsprojfid: Given the output of marsfidfinder, projects the XYZ locations and compares them to the found locations, creating a report showing the fiducial errors in each image. marsrad: Radiometrically corrects an image, (10) marsrelabel: Updates coordinate system or camera model labels in an image, (11) marstiexyz: Given a stereo pair, allows the user to interactively pick a point in each image and reports the XYZ value corresponding to that pair of locations. marsunmosaic: Extracts a single frame from a mosaic, which will be created such that it could have been an input to the original mosaic. Useful for creating simulated input frames using different camera models than the original mosaic used, and (12) merinverter: Uses an inverse lookup table to convert 8-bit telemetered data to its 12-bit original form. Can be used in other missions despite the name.

Deen, Robert G.↗
NASA NTRSDate not supplied

RGB Imaging as a Tool to Monitor Indoor Crop Plant Production

Future crop production in space will require robust monitoring technologies that can optimize crop yield, reduce waste, and generate data for an automated plant growth design. Imaging has been suggested as a tool for measuring plant health, yet imaging systems for indoor crops have not been tested in spaceflight. Fortunately, RGB images of crop plants growing inside the Advanced Plant Habitat (APH) aboard the ISS have already been captured. In ground-based studies, the Kennedy Space Center (NASA, KSC) is collaborating with the United States Department of Agriculture (USDA ARS) to develop an imaging system for monitoring indoor crop plant health. In one study, we applied a drought stress to ‘Dragoon’ lettuce plants over a period of 14 days and captured RGB images in 24 h increments. Images were analyzed, and by applying a difference index, the images were able to be used to detect the drought stress in lettuce. This difference index was then applied to RGB images collected inside the APH ground unit for a pre-flight experiment growing ‘Outredgous’ lettuce under different substrate moisture conditions, and results showed that the RGB camera was capable of detecting drought stress inside the spaceflight plant growth hardware. These results suggest that RGB cameras already deployed to space may offer valuable information for monitoring plant production in extraterrestrial environments. This research was supported by NASA’s space biology program.

Rachel Tucker↗
NASA NTRS1999

Detector for Particle Surface Contamination

A system and method for detecting and quantizing particle fallout contamination particles which are collected on a transparent disk or other surface employs an optical detector, such as a CCD camera, to obtain images of the disk and a computer for analyzing the images. From the images, the computer detects, counts and sizes particles collected on the disk The computer also determines, through comparison to previously analyzed images, the particle fallout rate, and generates an alarm or other indication if the rate exceeds a maximum allowable value. The detector and disk are disposed in a housing having an aperture formed therein for defining the area on the surface of the disk which is exposed to the particle fallout. A light source is provided for evenly illuminating the disk. A first drive motor slowly rotates the disk to increase the amount of its surface area which is exposed through the aperture to the particle fallout. A second motor is also provided for incrementally scanning the disk in a radial direction back and forth over the camera so that the camera eventually obtains images of the entire surface of the disk which is exposed to the particle fallout.

Mogan, Paul A.↗
NASA NTRS2004

Application of Geostatistical Simulation to Enhance Satellite Image Products

With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

Hlavka, Christine A.↗
NASA NTRSDate not supplied

Visualizing UPSP Data with Python

The Unsteady Pressure-Sensitive Paint (uPSP) projects uses Pressure-Sensitive paint applied over aerospace models during wind tunnel testing to collect pressure data with high spatial and temporal resolution in order to inform unsteady aerodynamics studies. For each of the 800+ experimental runs, four cameras generate up to 50 GB of video data, which must then be processed, analyzed, and visualized on the NASA Advanced Supercomputing system (NAS) to assess the result. One of the final data analysis products is the dynamic modal decomposition (DMD) results, which decomposes the pressure reading signals by their frequency component. The goal of this project is to visualize the DMD results over a 3D rendering of the model, using efficient and parallelized python routines. The software uses the pytecplot library, a high-level API that connects python scripting to a Tecplot 360 engine. Tecplot is an industry standard high-performance visualization tool that can handle large datasets and workflow. Various animation, rendering, and image-combination techniques were investigated to generate the final videos using OpenCV on the NAS. The final result is a software tool that takes in data products from the uPSP processing chain and generates high resolution visualization videos in parallel for every data file, allowing researchers to view their results efficiently and at an unprecedentedly detailed level.

Emma Dolores McMillian↗
NASA NTRS2018

Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN.

Dolph, Chester V.↗
NASA NTRS2021

Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Geostationary satellites↗
NASA NTRS1993

Martian surface simulations

Computer generated surfaces have been created to aid in imaging, landing and rover studies for Mars and the moon. They are also being applied to the study of cratering histories. The surfaces are generated in steps which attempt to mimic geologic episodes. Surface roughness is realized fractally, while craters and other specific features have shapes and distributions dictated by observation. Surface materials are assigned appropriate albedos, making the images more realistic. With the inclusion of correlations between crater and rock distributions, the simulations are beginning to acquire a predictive capability.

Gaskell, Robert W.↗
NASA NTRSDate not supplied

An Overview of SHERLOC Raman and Fluorescence Spectroscopy Results Obtained During Perseverance’s Green Zone Campaign at Jezero Crater, Mars

The Perseverance rover landed in Jezero crater, Mars, in February 2021. The field site was chosen because orbiter data provided evidence that the crater hosted an ancient (>2.7 Ga) fluvio-lacustrine environment. The Octavia E. Butler landing site is located ~1.9 km east of the erosional remnant of the Jezero river delta. Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is an arm-mounted instrument that combines fluorescence and Raman spectroscopy with microscopic imaging to analyze geological materials in order to better understand the history of the environments recorded in the rocks within Jezero crater and to search for potential biosignatures. SHERLOC has two microscopic cameras, the Autofocus and Context Imager (ACI) and the Wide-Angle Topographic Sensor for Operations and eNgineering, (WATSON). These subsystems obtain high spatial resolution (10.1–100 µm/pixel) images to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially-resolved correlation of trace organic materials. SHERLOC’s 248.6 nm deep UV laser generates a 100 µm-diameter spot. Photons generated by Raman scattering and fluorescence emission are collected and the spectra are downlinked to Earth for analysis. Knowledge of where the laser is pointed allows for mineral and compositional maps to be generated and overlain in ACI and WATSON images. Early spectroscopy observations focused on natural rock surfaces at targets named Nataani (sol 83), Bi_la_sana (sol 98) and Foux (sol 141). These surfaces included patches of aeolian dust that had settled upon the rocks. Perseverance’s abrasion tool is expected to become available in August 2021; it will grind to depths of 14 mm to remove dust and penetrate coatings and weathering rinds. This presentation will summarize major results from analysis of the rocks examined by SHERLOC during Perseverance’s first science and sample coring campaign (Green Zone Campaign).

Luther W Beegle↗
NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗
NASA NTRS1980

Effects of magnification and visual accommodation on aimpoint estimation in simulated landings with real and virtual image displays

Twenty professional pilots observed a computer-generated airport scene during simulated autopilot-coupled night landing approaches and at two points (20 sec and 10 sec before touchdown) judged whether the airplane would undershoot or overshoot the aimpoint. Visual accommodation was continuously measured using an automatic infrared optometer. Experimental variables included approach slope angle, display magnification, visual focus demand (using ophthalmic lenses), and presentation of the display as either a real (direct view) or a virtual (collimated) image. Aimpoint judgments shifted predictably with actual approach slope and display magnification. Both pilot judgments and measured accommodation interacted with focus demand with real-image displays but not with virtual-image displays. With either type of display, measured accommodation lagged far behind focus demand and was reliably less responsive to the virtual images. Pilot judgments shifted dramatically from an overwhelming perceived-overshoot bias 20 sec before touchdown to a reliable undershoot bias 10 sec later.

Randle, R. J.↗
NASA NTRS2017

Landsat 8 Remote Sensing Reflectance (Rrs) Products: Evaluations, Intercomparisons, and Enhancements

The Operational Land Imager (OLI) onboard Landsat-8 is generating high-quality aquatic science products, the most critical of which is the remote sensing reflectance (Rrs), defined as the ratio of water-leaving radiance to the total downwelling irradiance just above water. The quality of the Rrs products has not, however, been extensively assessed. This manuscript provides a comprehensive evaluation of Level-1B, i.e., top of atmosphere reflectance, and Rrs products available from OLI imagery under near-ideal atmospheric conditions in moderately turbid waters. The procedure includes a) evaluations of the Rrs products at sites included in the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), b) intercomparisons and cross-calibrations against other ocean color products, and c) optimizations of vicarious calibration gains across the entire OLI observing swath. Results indicate that the near-infrared and shortwave infrared (NIR-SWIR) band combinations yield the most robust and stable Rrs retrievals in moderately turbid waters. Intercomparisons against products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA) indicate slight across-track non-uniformities (<1%) associated with OLI scenes in the blue bands. In both product domains (TOA and Rrs), on average, the OLI products were found larger in radiometric responses in the blue channels. Following the implementation of updated vicarious calibration gains and accounting for across-track non-uniformities, matchup analyses using independent in-situ validation data confirmed improvements in Rrs products. These findings further support high-fidelity OLI-derived aquatic science products in terms of both demonstrating a robust atmospheric correction method and providing consistent products across OLI's imaging swath.

Pahlevan, Nima↗
NASA NTRS2007

Development of Natural Flaw Samples for Evaluating Nondestructive Testing Methods for Foam Thermal Protection Systems

Low density polyurethane foam has been an important insulation material for space launch vehicles for several decades. The potential for damage from foam breaking away from the NASA External Tank was not realized until the foam impacts on the Columbia Orbiter vehicle caused damage to its Leading Edge thermal protection systems (TPS). Development of improved inspection techniques on the foam TPS is necessary to prevent similar occurrences in the future. Foamed panels with drilled holes for volumetric flaws and Teflon inserts to simulate debonded conditions have been used to evaluate and calibrate nondestructive testing (NDT) methods. Unfortunately the symmetric edges and dissimilar materials used in the preparation of these simulated flaws provide an artificially large signal while very little signal is generated from the actual defects themselves. In other words, the same signal are not generated from the artificial defects in the foam test panels as produced when inspecting natural defect in the ET foam TPS. A project to create more realistic voids similar to what actually occurs during manufacturing operations was began in order to improve detection of critical voids during inspections. This presentation describes approaches taken to create more natural voids in foam TPS in order to provide a more realistic evaluation of what the NDT methods can detect. These flaw creation techniques were developed with both sprayed foam and poured foam used for insulation on the External Tank. Test panels with simulated defects have been used to evaluate NDT methods for the inspection of the External Tank. A comparison of images between natural flaws and machined flaws generated from backscatter x-ray radiography, x-ray laminography, terahertz imaging and millimeter wave imaging show significant differences in identifying defect regions.

Workman, Gary L.↗
NASA NTRS1986

Analysis of the dynamics of African vegetation using the normalized difference vegetation index

Images at a resolution of 8 km are currently being generated for the whole of Africa, displaying the normalized difference vegetation index (NDVI). These images have undergone a process of temporal compositing to reduce the effects of cloud cover and atmospheric variation. When the NDVI is plotted against time, different cover types are shown to have characteristic profiles corresponding closely with their phenology. The resultant pattern of NDVI values displayed on the images is analyzed in terms of the cover types present and local variations in rainfall. Comparison between images for 1983 and 1984 overall showed considerable similarities, but significant differences were observed in the northward extent of the greening wave in the Sahel, the greening up of the Kalahari Desert and East African communities. It is concluded that vegetation monitoring using NDVI images needs to be associated with scene stratification according to cover type.

Townshend, J. R. G.↗
NASA NTRS2004

Variable Shadow Screens for Imaging Optical Devices

Variable shadow screens have been proposed for reducing the apparent brightnesses of very bright light sources relative to other sources within the fields of view of diverse imaging optical devices, including video and film cameras and optical devices for imaging directly into the human eye. In other words, variable shadow screens would increase the effective dynamic ranges of such devices. Traditionally, imaging sensors are protected against excessive brightness by use of dark filters and/or reduction of iris diameters. These traditional means do not increase dynamic range; they reduce the ability to view or image dimmer features of an image because they reduce the brightness of all parts of an image by the same factor. On the other hand, a variable shadow screen would darken only the excessively bright parts of an image. For example, dim objects in a field of view that included the setting Sun or bright headlights could be seen more readily in a picture taken through a variable shadow screen than in a picture of the same scene taken through a dark filter or a narrowed iris. The figure depicts one of many potential variations of the basic concept of the variable shadow screen. The shadow screen would be a normally transparent liquid-crystal matrix placed in front of a focal-plane array of photodetectors in a charge-coupled-device video camera. The shadow screen would be placed far enough from the focal plane so as not to disrupt the focal-plane image to an unacceptable degree, yet close enough so that the out-of-focus shadows cast by the screen would still be effective in darkening the brightest parts of the image. The image detected by the photodetector array itself would be used as feedback to drive the variable shadow screen: The video output of the camera would be processed by suitable analog and/or digital electronic circuitry to generate a negative partial version of the image to be impressed on the shadow screen. The parts of the shadow screen in front of those parts of the image with brightness below a specified threshold would be left transparent; the parts of the shadow screen in front of those parts of the image where the brightness exceeded the threshold would be darkened by an amount that would increase with the excess above the threshold.

Lu, Ed↗
NASA NTRS2012

Innovative Video Diagnostic Equipment for Material Science

Materials science experiments under microgravity increasingly rely on advanced optical systems to determine the physical properties of the samples under investigation. This includes video systems with high spatial and temporal resolution. The acquisition, handling, storage and transmission to ground of the resulting video data are very challenging. Since the available downlink data rate is limited, the capability to compress the video data significantly without compromising the data quality is essential. We report on the development of a Digital Video System (DVS) for EML (Electro Magnetic Levitator) which provides real-time video acquisition, high compression using advanced Wavelet algorithms, storage and transmission of a continuous flow of video with different characteristics in terms of image dimensions and frame rates. The DVS is able to operate with the latest generation of high-performance cameras acquiring high resolution video images up to 4Mpixels@60 fps or high frame rate video images up to about 1000 fps@512x512pixels.

Capuano, G.↗
NASA NTRSDate not supplied

Compressive Sensing Based Data Acquisition Architecture for Transient Stellar Events in Crowded Star Fields

Compressive sensing is a mathematical technique for simultaneous data acquisition and compression. In this work, we show a CS based architecture for acquiring and reconstructing transient stellar events. This architecture reconstructs a differenced image itself, eliminating the need for any sparse domain transforms, otherwise required for traditional CS reconstruction. The resulting reconstructed differenced image is of critical importance as the information required for generating a time-series photometric light curve is obtained only from a differenced image. Hence, reconstructing a crowded star spatial image, followed by differencing is wasteful. This architecture eliminates the need to 1.) transform an image to a sparse domain, 2.) Reconstruct a dense field, and then apply differencing on the image to obtain the image of critical value. We study the case of microlensing to depict a star source experiencing magnification in time. Our results show that this architecture is able to reconstruct star source magnitudes with magnification factors greater than 1 for clean images with error less than 2% using only 10% of the Nyquist rate samples.

Asmita Korde-Patel↗