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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Building a 2.5D Digital Elevation Model from 2D Imagery

When projecting imagery into a georeferenced coordinate frame, one needs to have some model of the geographical region that is being projected to. This model can sometimes be a simple geometrical curve, such as an ellipse or even a plane. However, to obtain accurate projections, one needs to have a more sophisticated model that encodes the undulations in the terrain including things like mountains, valleys, and even manmade structures. The product that is often used for this purpose is a Digital Elevation Model (DEM). The technology presented here generates a high-quality DEM from a collection of 2D images taken from multiple viewpoints, plus pose data for each of the images and a camera model for the sensor. The technology assumes that the images are all of the same region of the environment. The pose data for each image is used as an initial estimate of the geometric relationship between the images, but the pose data is often noisy and not of sufficient quality to build a high-quality DEM. Therefore, the source imagery is passed through a feature-tracking algorithm and multi-plane-homography algorithm, which refine the geometric transforms between images. The images and their refined poses are then passed to a stereo algorithm, which generates dense 3D data for each image in the sequence. The 3D data from each image is then placed into a consistent coordinate frame and passed to a routine that divides the coordinate frame into a number of cells. The 3D points that fall into each cell are collected, and basic statistics are applied to determine the elevation of that cell. The result of this step is a DEM that is in an arbitrary coordinate frame. This DEM is then filtered and smoothed in order to remove small artifacts. The final step in the algorithm is to take the initial DEM and rotate and translate it to be in the world coordinate frame [such as UTM (Universal Transverse Mercator), MGRS (Military Grid Reference System), or geodetic] such that it can be saved in a standard DEM format and used for projection.

Padgett, Curtis W.↗
NASA NTRS2006

Radiometric Characterization of the IKONOS, QuickBird, and OrbView-3 Sensors

Radiometric calibration of commercial imaging satellite products is required to ensure that science and application communities can better understand their properties. Inaccurate radiometric calibrations can lead to erroneous decisions and invalid conclusions and can limit intercomparisons with other systems. To address this calibration need, satellite at-sensor radiance values were compared to those estimated by each independent team member to determine the sensor's radiometric accuracy. The combined results of this evaluation provide the user community with an independent assessment of these commercially available high spatial resolution sensors' absolute calibration values.

Holekamp, Kara↗
NASA NTRS2020

SNPP and NOAA-20 VIIRS On-Orbit Geolocation Trending and Improvements

Two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors have been in operations for more than 8.5 and 2.5 years since they were launched in October 2011 on SNPP satellite and in November 2017 on NOAA-20 satellite, respectively. These are two satellites in the Join Polar Satellite System (JPSS) constellation, of which Suomi National Polar-orbiting Partnership (SNPP) is a risk reduction satellite and NOAA-20 is the first of four JPSS satellites(JPSS-1 became NOAA-20 after launch). Accurate geolocation is a critical element in data calibration for accurate retrieval of global biogeophysical parameters. In this paper, we describe the latest trends in the continuously improved geolocation accuracy in VIIRS Collection-1 (C1) and C2 re-processing. We implemented a VIIRS instrument geometric model update (VIGMU)for both sensors that correct for geolocation error oscilations in the scan direction. We borrowed code from Moderate Resolution Imaging Spectroradiometer (MODIS) geolocation software to correct for time-dependent pointing variations, that are particularly acute in NOAA-20 VIIRS, and some pointing anomalies in SNPP VIIRS. We developed a Kalman Filter using gyrodata to correct for attitude errors due to the degradation of the star trackers performance from the SNPP satellite. We also present an improved ground control point matching (CPM) tool, in which the ground control point (GCP) chips library is refreshed using recently launched Landsat-8 images.

SNPP↗
NASA NTRS2014

Satellite Ocean Color Sensor Design Concepts and Performance Requirements

In late 1978, the National Aeronautics and Space Administration (NASA) launched the Nimbus-7 satellite with the Coastal Zone Color Scanner (CZCS) and several other sensors, all of which provided major advances in Earth remote sensing. The inspiration for the CZCS is usually attributed to an article in Science by Clarke et al. who demonstrated that large changes in open ocean spectral reflectance are correlated to chlorophyll-a concentrations. Chlorophyll-a is the primary photosynthetic pigment in green plants (marine and terrestrial) and is used in estimating primary production, i.e., the amount of carbon fixed into organic matter during photosynthesis. Thus, accurate estimates of global and regional primary production are key to studies of the earth's carbon cycle. Because the investigators used an airborne radiometer, they were able to demonstrate the increased radiance contribution of the atmosphere with altitude that would be a major issue for spaceborne measurements. Since 1978, there has been much progress in satellite ocean color remote sensing such that the technique is well established and is used for climate change science and routine operational environmental monitoring. Also, the science objectives and accompanying methodologies have expanded and evolved through a succession of global missions, e.g., the Ocean Color and Temperature Sensor (OCTS), the Seaviewing Wide Field-of-view Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS), and the Global Imager (GLI). With each advance in science objectives, new and more stringent requirements for sensor capabilities (e.g., spectral coverage) and performance (e.g., signal-to-noise ratio, SNR) are established. The CZCS had four bands for chlorophyll and aerosol corrections. The Ocean Color Imager (OCI) recommended for the NASA Pre-Aerosol, Cloud, and Ocean Ecosystems (PACE) mission includes 5 nanometers hyperspectral coverage from 350 to 800 nanometers with three additional discrete near infrared (NIR) and shortwave infrared (SWIR) ocean aerosol correction bands. Also, to avoid drift in sensor sensitivity from being interpreted as environmental change, climate change research requires rigorous monitoring of sensor stability. For SeaWiFS, monthly lunar imaging accurately tracked stability at an accuracy of approximately 0.1% that allowed the data to be used for climate studies [2]. It is now acknowledged by the international community that future missions and sensor designs need to accommodate lunar calibrations. An overview of ocean color remote sensing and a review of the progress made in ocean color remote sensing and the variety of research applications derived from global satellite ocean color data are provided. The purpose of this chapter is to discuss the design options for ocean color satellite radiometers, performance and testing criteria, and sensor components (optics, detectors, electronics, etc.) that must be integrated into an instrument concept. These ultimately dictate the quality and quantity of data that can be delivered as a trade against mission cost. Historically, science and sensor technology have advanced in a "leap-frog" manner in that sensor design requirements for a mission are defined many years before a sensor is launched and by the end of the mission, perhaps 15-20 years later, science applications and requirements are well beyond the capabilities of the sensor. Section 3 provides a summary of historical mission science objectives and sensor requirements. This progression is expected to continue in the future as long as sensor costs can be constrained to affordable levels and still allow the incorporation of new technologies without incurring unacceptable risk to mission success. The IOCCG Report Number 13 discusses future ocean biology mission Level-1 requirements in depth.

Ocean Color↗
NASA NTRS2003

Automated Rock Identification for Future Mars Exploration Missions

A key task for human or robotic explorers on the surface of Mars is choosing which particular rock or mineral samples should be selected for more intensive study. The usual challenges of such a task are compounded by the lack of sensory input available to a suited astronaut or the limited downlink bandwidth available to a rover. Additional challenges facing a human mission include limited surface time and the similarities in appearance of important minerals (e.g. carbonates, silicates, salts). Yet the choice of which sample to collect is critical. To address this challenge we are developing science analysis algorithms to interface with a Geologist's Field Assistant (GFA) device that will allow robotic or human remote explorers to better sense and explore their surroundings during limited surface excursions. We aim for our algorithms to interpret spectral and imaging data obtained by various sensors. The algorithms, for example, will identify key minerals, rocks, and sediments from mid-IR, Raman, and visible/near-IR spectra as well as from high resolution and microscopic images to help interpret data and to provide high-level advice to the remote explorer. A top-level system will consider multiple inputs from raw sensor data output by imagers and spectrometers (visible/near-IR, mid-IR, and Raman) as well as human opinion to identify rock and mineral samples.

Gulick, V. C.↗
NASA NTRS1973

Imaging radars provide terrain texture and roughness parameters in semi-arid environments

Geoscience potentialities and limitations of available imaging radars are examined for alluvial valley-playa terrain environments. In such arid environments, a dual-sensor combination of an imaging radar with aerial photography is shown to provide a practical method for monitoring gross changes in surface textures of alluvial fans and playa surface conditions. It also appears feasible to infer surface materials and relative surface roughness with an improved degree of interpretive reliability.

Macdonald, H. C.↗
NASA NTRS1993

Multi-Angle Imaging SpectroRadiometer (MISR) Design Issues Influenced by Performance Requirements

The design of an Earth remote sensing sensor, such as the Multi-angle Imaging SpectroRadiometer (MISR), begins with a set of science requirements that determine a set of instrument specifications. It is required that the sensor meet these specifications across the image field, over a range of sensor operating temperatures, and throughout mission life. In addition, data quality must be maintained irrespective of bright objects, such as clouds, within the scene, or out-of-field glint sources. During the design phase of MISR, many refinements to the conceptual design have been made to insure that these performance criteria are met. These design considerations are the focus of this paper. Spectral stability with field angle, scene polarization insensitivity, and LTV exposure hardness have, for example, been enabled through a telecentric optical design, a gaussian shaped filter spectral profile used in conjunction with a Lyot depolarizer, and contamination prevention through consideration of material choices and handling procedures. Spectral, radiometric, and MTF stability of the instrument assures the scientific community that MISR imagery can be used for highly accurate aerosol, bi-directional reflectance distribution function (BRDF), and cloud studies.

Bruegge, Carol J.↗
NASA NTRSDate not supplied

Development of Multi-Edge Slant Target for Unlocalized MTF Measurement of Airborne Imaging System Payloads

Calibration of imaging instruments is a critical step in effectively characterizing imaging data. A comprehensive characterization of an imaging system provides valuable insight into the overall uncertainties in the results derived from the raw data collected. The Scientifically Calibrated In-Flight Imagery (SCIFLI) team (based at NASA Langley Research Center) has designed, built, tested, and successfully flown the optical payload known as the SCIFLI Airborne Multispectral Imager (SAMI). SAMI is a multispectral imaging payload, complete with sensor configurations for the Ultraviolet-Visible spectrum as well as Near- Infrared, Shortwave Infrared, and Midwave Infrared bands and is fitted with various optical hardware designed to turn, split, and focus light beams to the respective in-band sensors. Radiometric calibrations are performed for SAMI such that the accuracy and uncertainty of thermal and spectral measurements are effectively characterized. However, SAMI does not regularly undergo spatial characterizations to evaluate the performance of the imager at high spatial frequencies. This is essential, as understanding the spatial sensor response of discrete sensors is just as important as the spectral sensor response. The payload currently utilizes a calibration cart for lab testing. Equipped with a modular setup and several radiation sources, it is perfect for performing high-quality radiometric calibrations. This work focuses on using the calibration cart to perform spatial calibrations. A custom designed calibration target is being used to evaluate the optical performance of SAMI. Specifically, the target is designed to measure the Modulation Transfer Function (MTF) of the imager in discrete locations around the focal plane. The results of this characterization will provide insight into the spatial resolution capabilities of the SAMI payload in practice. Future work includes using the results of the study to correct optical imperfections in imagery taken with SAMI.

SAMI↗
NASA NTRS1987

Spaceborne imaging radar research in the 1990s - An overview

Research and proposed experiments for improving the capabilities of spaceborne imaging radars are discussed. The development of multiparameter research sensors, long-term and global monitoring sensors, planetary mapping sensors, and topographic three-dimensional imagers is examined. The properties and functions of these proposed sensors are described; examples of these various types of sensors are provided.

Elachi, Charles↗
NASA NTRS1979

Map-guided interpretation of remotely-sensed imagery

A map-guided approach to interpretation of remotely sensed imagery is described, with emphasis on applications involving continuous monitoring of predetermined ground sites. Geometric correspondence between a sensed image and a symbolic reference map is established in an initial stage of processing by adjusting parameters of a sensor model so that image features predicted from the map optimally match corresponding features extracted from the sensed image. Information in the map is then used to constrain where to look in an image and what to look for. With such constraints, previously intractable remote sensing tasks can become feasible, even easy, to automate. Four illustrative examples are given, involving the monitoring of reservoirs, roads, railroad yards, and harbors.

Tenenbaum, J. M.↗
NASA NTRS1991

Parallel Processing System

In order to process very high resolution image data from spacecraft sensors, Goddard Space Flight Center commissioned the development of a Massively Parallel Processor (MPP) based upon simultaneous processing of image picture elements (pixels) rather than serial processing. It resulted in a considerable increase in computational speed. MasPar Computer Corporation's MasPar MP-1 incorporates this technology, allowing users to attack a variety of computationally-intensive problems. The MP-1 is no longer manufactured but has been replaced by the MP-2, a more advanced model.

Source record↗
NASA NTRS2011

NASA Tech Briefs, November 2011

The topics include: 1) Flight Test Results from the Rake Airflow Gage Experiment on the F-15B; 2) Telemetry and Science Data Software System; 3) CropEx Web-Based Agricultural Monitoring and Decision Support; 4) High-Performance Data Analysis Tools for Sun-Earth Connection Missions; 5) Experiment in Onboard Synthetic Aperture Radar Data Processing; 6) Microfabrication of a High-Throughput Nanochannel Delivery/Filtration System; 7) Improved Design and Fabrication of Hydrated-Salt Pills; 8) Monolithic Flexure Pre-Stressed Ultrasonic Horns; 9) Cryogenic Quenching Process for Electronic Part Screening; 10) Broadband Via-Less Microwave Crossover Using Microstrip-CPW Transitions; 11) Wheel-Based Ice Sensors for Road Vehicles; 12) G-DYN Multibody Dynamics Engine; 13) Multibody Simulation Software Testbed for Small-Body Exploration and Sampling; 14) Propulsive Reaction Control System Model; 15) Licklider Transmission Protocol Implementation; 16) Core Recursive Hierarchical Image Segmentation; 17) Two-Stage Centrifugal Fan; 18) Combined Structural and Trajectory Control of Variable-Geometry Planetary Entry Systems; 19) Pressure Regulator With Internal Ejector Circulation Pump, Flow and Pressure Measurement Porting, and Fuel Cell System Integration Options; 20) Temperature-Sensitive Coating Sensor Based on Hematite; 21) Standardization of a Volumetric Displacement Measurement for Two-Body Abrasion Scratch Test Data Analysis; 22) Detection of Carbon Monoxide Using Polymer-Carbon Composite Films; 23) Substituted Quaternary Ammonium Salts Improve Low-Temperature Performance of Double-Layer Capacitors; 24) Sustainably Sourced, Thermally Resistant, Radiation Hard Biopolymer; 25) Integrated Lens Antennas for Multi-Pixel Receivers; 26) 180-GHz Interferometric Imager; 27) Maturation of Structural Health Management Systems for Solid Rocket Motors; 28) Validating Phasing and Geometry of Large Focal Plane Arrays; 29) Transverse Pupil Shifts for Adaptive Optics Non-Common Path Calibration; 30) Qualification of Fiber Optic Cables for Martian Extreme Temperature Environments; 31) Solid-State Spectral Light Source System; 32) Multiple-Event, Single-Photon Counting Imaging Sensor; 33) Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations; and 34) Achieving Exact and Constant Turnaround Ratio in a DDS-Based Coherent Transponder.

Source record↗
NASA NTRS2012

Calibration of VIIRS F1 Sensor Fire Detection Band Using lunar Observations

Visible Infrared Imager Radiometer Suite (VIIRS) Fight 1 (Fl) sensor includes a fire detection band at roughly 4 microns. This spectral band has two gain states; fire detection occurs in the low gain state above approximately 345 K. The thermal bands normally utilize an on-board blackbody to provide on-orbit calibration. However, as the maximum temperature of this blackbody is 315 K, the low gain state of the 4 micron band cannot be calibrated in the same manner as the rest of the thermal bands. Regular observations of the moon provide an alternative calibration source. The lunar surface temperature has been recently mapped by the DIVINER sensor on the LRO platform. The periodic on-board high gain calibration along with the DIVINER surface temperatures was used to determine the emissivity and solar reflectance of the lunar surface at 4 microns; these factors and the lunar data are then used to fit the low gain calibration coefficients of the 4 micron band. Furthermore, the emissivity of the lunar surface is well known near 8.5 microns due to the Christiansen feature (an emissivity maximum associated with Si-O stretching vibrations) and the solar reflectance is negligible. Thus, the 8.5 micron band is used for relative calibration with the 4 micron band to de-trend any temporal variations. In addition, the remaining thermal bands are analyzed in a similar fashion, with both calculated emissivities and solar reflectances produced.

McIntire, Jeff↗
NASA NTRS1981

Remote sensing of the diffuse attenuation coefficient of ocean water

A technique was devised which uses remotely sensed spectral radiances from the sea to assess the optical diffuse attenuation coefficient, K (lambda) of near-surface ocean water. With spectral image data from a sensor such as the coastal zone color scanner (CZCS) carried on NIMBUS-7, it is possible to rapidly compute the K (lambda) fields for large ocean areas and obtain K "images" which show synoptic, spatial distribution of this attenuation coefficient. The technique utilizes a relationship that has been determined between the value of K and the ratio of the upwelling radiances leaving the sea surface at two wavelengths. The relationship was developed to provide an algorithm for inferring K from the radiance images obtained by the CZCS, thus the wavelengths were selected from those used by this sensor, viz., 443, 520, 550 and 670 nm. The majority of the radiance arriving at the spacecraft is the result of scattering in the atmospheric and is unrelated to the radiance signal generated by the water. A necessary step in the processing of the data received by the sensor is, therefore, the effective removal of these atmospheric path radiance signals before the K algorithm is applied. Examples of the efficacy of these removal techniques are given together with examples of the spatial distributions of K in several ocean areas.

Austin, R. W.↗
NASA NTRSDate not supplied

Wichita Climate: Using Satellite Data to Identify Neighborhoods Vulnerable to Extreme Heat for Equitable Climate Mitigation and Planning

Wichita, Kansas is facing a host of climate threats, one being extreme heat that is manifested through the urban heat island (UHI) effect. The uneven distribution of heat risk in Wichita across socioeconomic status is an environmental justice issue. We worked with the City of Wichita to map heat exposure, tree canopy, and heat risk in order to support the City's climate resilience initiatives. To visualize heat exposure, we quantified and mapped average summer heat from 2013–2021 using Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Land Surface Temperature (LST) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) night-time LST. To understand tree canopy cover gaps, we created a tree canopy map using 2021 PlanetScope imagery, which identified 20% more trees than the US Geological Survey’s (USGS) National Land Cover Database (NLCD) tree canopy coverage estimates for Wichita. To characterize high risk areas, we used socioeconomic census data and existing social vulnerability indices, highlighting populations that were exposed and vulnerable to extreme heat. The spatial analyses demonstrated that heat exposure is concentrated in the city center and southwest Wichita, areas that are also low in tree canopy coverage. The three census block groups and 17 census tracts with the highest heat risk primarily circle the city center, in areas home to more socially vulnerable populations and near enough to the dense urban center to feel significant urban heat island effects.

Brooke Laird↗
NASA NTRS2022

Wichita Climate: Using Satellite Data to Identify Neighborhoods Vulnerable to Extreme Heat for Equitable Climate Mitigation and Planning

Wichita, Kansas is facing a host of climate threats, one being extreme heat that is manifested through the urban heat island (UHI) effect. The uneven distribution of heat risk in Wichita across socioeconomic status is an environmental justice issue. We worked with the City of Wichita to map heat exposure, tree canopy, and heat risk in order to support the City's climate resilience initiatives. To visualize heat exposure, we quantified and mapped average summer heat from 2013–2021 using Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Land Surface Temperature (LST) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) night-time LST. To understand tree canopy cover gaps, we created a tree canopy map using 2021 PlanetScope imagery, which identified 20% more trees than the US Geological Survey’s (USGS) National Land Cover Database (NLCD) tree canopy coverage estimates for Wichita. To characterize high risk areas, we used socioeconomic census data and existing social vulnerability indices, highlighting populations that were exposed and vulnerable to extreme heat. The spatial analyses demonstrated that heat exposure is concentrated in the city center and southwest Wichita, areas that are also low in tree canopy coverage. The three census block groups and 17 census tracts with the highest heat risk primarily circle the city center, in areas home to more socially vulnerable populations and near enough to the dense urban center to feel significant urban heat island effects.

Brooke Laird↗