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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Remote sensing of the ocean waveheight spectrum using synthetic-aperture-radar images

The paper discusses problems in the detection and measurement of ocean waves from their SAR images, in particular the measurement of the wavenumber spectrum of ocean-wave-height fluctuations. Comparisons on a limited set of pitch-roll buoy and SEASAT SAR measurements during the 1978 JASIN experiment reveal that degradation of SAR resolution caused by wave orbital motion is a crucial factor in the detection of waves by SAR images. Thus, waves with small slopes traveling perpendicular to the SAR flight path are more easily detected than waves with large slopes traveling along the flight path. Although the SAR estimates in this comparison were found to contain significant biases, they were in rough agreement with buoy measurements, provided the ocean wavelength was between about 120 and 400 m and the ocean wave direction was not approximately along the SAR flight path.

Vesecky, J. F.↗
NASA NTRS2004

Spaceborne Microwave Remote Sensing of Seasonal Freeze-Thaw Processes in the Terrestrial High Latitudes: Relationships with Land-Atmosphere CO2 exchange

Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These relatively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, separately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North America and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO2 concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, though both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.

net primary products↗
NASA NTRS1981

Onboard utilization of ground control points for image correction. Volume 2: Analysis and simulation results

An approach to remote sensing that meets future mission requirements was investigated. The deterministic acquisition of data and the rapid correction of data for radiometric effects and image distortions are the most critical limitations of remote sensing. The following topics are discussed: onboard image correction systems, GCP navigation system simulation, GCP analysis, and image correction analysis measurement.

Source record↗
NASA NTRS1974

Iconics and iconology

The term iconics covers the complex of methods and equipment for obtaining images, integrated on a common methodological basis. The concept combines such methods and equipment for obtaining images as photography, television, phototelevision, thermovision, and radar. The term iconics has been used previously, but in a narrower sense, as the name for the theory of image reproduction systems, primarily in the sense of their optimization. Iconics must include two branches; theoretical and practical. The system optimization problem then takes its place in theoretical iconics along with other problems. The term iconology has been proposed to cover the complex of image interpretation methods and equipment.

Rodionov, B. N.↗
NASA NTRS2017

Searching for Water Ice at the Lunar North Pole Using High-Resolution Images and Radar

Permanently shadowed regions (PSRs) at the lunar poles are potential reservoirs of frozen volatiles, and are therefore high-priority exploration targets. PSRs trap water and other volatiles because their annual maximum temperatures (40-100K) are lower than the sublimation temperatures of these species (i.e. H2O approx.104K). Previous studies using various remote sensing techniques have not been able to definitively characterize the distribution or abundance of ice in lunar PSRs. The purpose of this study is to search for signs of ice in PSRs using two complimentary remote sensing techniques: radar and visible images.

Mitchell, J. L.↗
NASA NTRS1989

Radiometric sensitivity comparisons of multispectral imaging systems

Multispectral imaging systems provide much of the basic data used by the land and ocean civilian remote-sensing community. There are numerous multispectral imaging systems which have been and are being developed. A common way to compare the radiometric performance of these systems is to examine their noise-equivalent change in reflectance, NE Delta-rho. The NE Delta-rho of a system is the reflectance difference that is equal to the noise in the recorded signal. A comparison is made of the noise equivalent change in reflectance of seven different multispectral imaging systems (AVHRR, AVIRIS, ETM, HIRIS, MODIS-N, SPOT-1, HRV, and TM) for a set of three atmospheric conditions (continental aerosol with 23-km visibility, continental aerosol with 5-km visibility, and a Rayleigh atmosphere), five values of ground reflectance (0.01, 0.10, 0.25, 0.50, and 1.00), a nadir viewing angle, and a solar zenith angle of 45 deg.

Lu, Nadine C.↗
NASA NTRS1997

The Dust-to-Gas Ratio in the Damped Ly alpha Clouds Towards the Gravitationally Lensed QSO 0957+561

We present HST/FOS spectra of the two bright images (A and B) of the gravitationally lensed QSO 0957+561 in the wavelength range 2200-3300 A. We find that the absorption system (Z(sub abs)) = 1.3911) near z(sub em) is a weak, damped Ly alpha system with strong Ly alpha absorption lines seen in both images. However, the H(I) column densities are different, with the line of sight to image A intersecting a larger column density. The continuum shapes of the two spectra differ in the sense that the flux level of image A increases more slowly toward shorter wavelengths than that of image B. We explain this as the result of differential reddening by dust grains in the damped Ly alpha absorber. A direct outcome of this explanation is a determination of the dust-to-gas ratio, k, in the damped Ly alpha system. We derive k = 0.55 + 0.18 for a simple 1/lambda extinction law and k = 0.31 + 0.10 for the Galactic extinction curve. For gravitationally lensed systems with damped Ly alpha absorbers, our method is a powerful tool for determining the values and dispersion of k, and the shapes of extinction curves, especially in the FUV and EUV regions. We compare our results with previous work.

Zuo, Lin↗
NASA NTRS1992

Expert system-based mineral mapping using AVIRIS

Integrated analysis of imaging spectrometer data and field spectral measurements were used in conjunction with conventional geologic field mapping to characterize bedrock and surficial geology at the northern end of Death Valley, California and Nevada. A knowledge-based expert system was used to automatically produce image maps from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data showing the principal surface mineralogy. The imaging spectrometer data show the spatial distribution of spectrally distinct minerals occurring both as primary rock-forming minerals and as alteration and weathering products. Field spectral measurements were used to verify the mineral maps and field mapping was used to extend the remote sensing results. Geographically referenced image-maps produced from these data form new base maps from which to develop improved understanding of the processes of deposition and erosion affecting the present land surface. The 'northern Grapevine Mountains' (NGM) study area was reported on in numerous papers. This area is an unnamed northwestward extension of the range. Most of the research here has concentrated on mapping of Jurassic-age plutons and associated hydrothermal alteration, however, the nature and scope of these studies is much broader, pertaining to the geologic history and development of the entire Death Valley region. AVIRIS data for the NGM site were obtained during May 1989. Additional AVIRIS data were acquired during September 1989 as part of the Geologic Remote Sensing Field Experiment (GRSFE). The area covered by these data overlaps slightly with the May 1989 data. Three and one-half AVIRIS scenes total were analyzed.

Kruse, Fred A.↗
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.↗
DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗
NASA NTRS1977

System implications of large radiometric array antennas

Temperature sensitivity degradation due to antenna losses is recognized as a hindrance to extension of available remote-sensing radiometry techniques to larger systems for enhanced spatial resolution. The use of large antennas is also complicated by difficulties with bandwidth and integration time. Advantages of introducing active devices at the subarray level, with distributed Dicke switching or correlation detection, are examined, and their possible contribution to the design of effective imaging microwave radiometers for remote sensing of geophysical parameters (sea state, distribution of sea ice, cloud types and their distributions, soil moisture) is assessed.

Levis, C. A.↗
NASA NTRS1992

Remote sensing of cloud, aerosol and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS)

The Moderate Resolution Imaging Spectrometer (MODIS) is an Earth-viewing sensor being developed as a facility instrument for the Earth Observing System (EOS) to be launched in the late 1990s. MODIS consists of two separate instruments that scan a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, Sun-synchronous, platform at an altitude of 705 km. Of primary interest for studies of atmospheric physics is the MODIS-N (nadir) instrument which will provide images in 36 spectral bands between 0.415 and 14.235 micrometers with spatial resoulutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean and atmosperhic processes. The intent of this lecture is to describe the current status of MODIS-N and its companion instrument MODIS-T (tilt), a tiltable cross-track scanning radiometer with 32 uniformly spaced channels between 0.410 and 0.875 micrometers, and to describe the physical principles behind the development of MODIS for the remote sensing of atmospheric properties. Primary emphasis will be placed on the main atmospheric applications of determining the optical, microphysical and physical properties of clouds and aerosol particles form spectral-reflection and thermal-emission measurements. In addition to cloud and aerosol properties, MODIS-N will be utilized for the determination of the total precipitable water vapor over land and atmospheric stability. The physical principles behind the determination of each of these atmospheric products will be described herein.

King, M. D.↗
NASA NTRS2010

Image Segmentation Analysis for NASA Earth Science Applications

NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

Tilton, James C.↗
NASA NTRS1994

BIOME: An Ecosystem Remote Sensor Based on Imaging Interferometry

Until recent times, optical remote sensing of ecosystem properties from space has been limited to broad band multispectral scanners such as Landsat and AVHRR. While these sensor data can be used to derive important information about ecosystem parameters, they are very limited for measuring key biogeochemical cycling parameters such as the chemical content of plant canopies. Such parameters, for example the lignin and nitrogen contents, are potentially amenable to measurements by very high spectral resolution instruments using a spectroscopic approach. Airborne sensors based on grating imaging spectrometers gave the first promise of such potential but the recent decision not to deploy the space version has left the community without many alternatives. In the past few years, advancements in high performance deep well digital sensor arrays coupled with a patented design for a two-beam interferometer has produced an entirely new design for acquiring imaging spectroscopic data at the signal to noise levels necessary for quantitatively estimating chemical composition (1000:1 at 2 microns). This design has been assembled as a laboratory instrument and the principles demonstrated for acquiring remote scenes. An airborne instrument is in production and spaceborne sensors being proposed. The instrument is extremely promising because of its low cost, lower power requirements, very low weight, simplicity (no moving parts), and high performance. For these reasons, we have called it the first instrument optimized for ecosystem studies as part of a Biological Imaging and Observation Mission to Earth (BIOME).

Peterson, David L.↗
NASA NTRS1996

(abstract) Detection of Mesoscale Oceanic Phenomena in the Southern California Bight via ERS-1 SAR

ERS-1 SAR images are examined with the intent of identifying mesoscale oceanic phenomena in the Southern California Bight (SCB). The SCB is a region of dynamic physical and biological processes, and as such has long been studied. The ERS-1 SAR images are augmented with other remote sensing data, including SIR-C SAR, AVHRR, ERS-1 ATSR, and Space Shuttle hand-held photography. Analysis of these images has revealed a more dynamic mesoscale field than was previously suspected. Eddies with scales on the order of 20-30 km in diameter have been the most common mesoscale features evident, but the presence of oceanic fronts and internal waves, as well as atmospheric internal waves, have been documented as well. The multisensor data record will provide insight as to the underlying ocean and meterological conditions that lead to the prevalence especially of the eddies in this region.

Southern California Bight SCB remote sensing data ↗
NASA NTRSDate not supplied

Evaluation of Aqua MODIS and S-NPP VIIRS Thermal Emissive Bands Calibration Stability Using Dome-C

Establishing the calibration consistency between satellite measurements is an essential step in the implementation of a long-term global monitoring plan, which often leads to sensor calibration improvements. The Suomi National Polar-Orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is a polar-orbiting Earth remote sensing instrument built with a strong Moderate Resolution Imaging Spectroradiometer (MODIS) heritage. The center wavelengths of all VIIRS thermal emissive bands (TEBs) match well with those of MODIS with the exception of VIIRS TEB M15 (10.7μm). Previous work from the MODIS Characterization Support Team (MCST) at the NASA/GSFC used specific Earth surface targets to track the long-term consistency,stability, and relative bias between the two MODIS instruments onboard the Terra and Aqua satellites. Using similar methodologies, this paper evaluates the TEB calibration consistency between the MODIS instruments and S-NPP VIIRS over Dome Concordia (Dome-C). The Dome-C site is uniformly snow-covered and the atmospheric effects are small in the surrounding area. Near-surface air temperature measurements from an Automatic Weather Station (AWS) are used as a reference to track each sensor’s calibration stability and determine the relative bias between the MODIS and VIIRS instruments. The results of this study provide a quantitative assessment of the S-NPP VIIRS TEB mission-long performance.

MODIS↗
NASA NTRS2014

Human Exploration Science Office (KX) Overview

The Human Exploration Science Office supports human spaceflight, conducts research, and develops technology in the areas of space orbital debris, hypervelocity impact technology, image science and analysis, remote sensing, imagery integration, and human and robotic exploration science. NASA's Orbital Debris Program Office (ODPO) resides in the Human Exploration Science Office. ODPO provides leadership in orbital debris research and the development of national and international space policy on orbital debris. The office is recognized internationally for its measurement and modeling of the debris environment. It takes the lead in developing technical consensus across U.S. agencies and other space agencies on debris mitigation measures to protect users of the orbital environment. The Hypervelocity Impact Technology (HVIT) project evaluates the risks to spacecraft posed by micrometeoroid and orbital debris (MMOD). HVIT facilities at JSC and White Sands Test Facility (WSTF) use light gas guns, diagnostic tools, and high-speed imagery to quantify the response of spacecraft materials to MMOD impacts. Impact tests, with debris environment data provided by ODPO, are used by HVIT to predict risks to NASA and commercial spacecraft. HVIT directly serves NASA crew safety with MMOD risk assessments for each crewed mission and research into advanced shielding design for future missions. The Image Science and Analysis Group (ISAG) supports the International Space Station (ISS) and commercial spaceflight through the design of imagery acquisition schemes (ground- and vehicle-based) and imagery analyses for vehicle performance assessments and mission anomaly resolution. ISAG assists the Multi-Purpose Crew Vehicle (MPCV) Program in the development of camera systems for the Orion spacecraft that will serve as data sources for flight test objectives that lead to crewed missions. The multi-center Imagery Integration Team is led by the Human Exploration Science Office and provides expertise in the application of engineering imagery to spaceflight. The team links NASA programs and private industry with imagery capabilities developed and honed through decades of human spaceflight, including imagery integration, imaging assets, imagery data management, and photogrammetric analysis. The team is currently supporting several NASA programs, including commercial demonstration missions. The Earth Science and Remote Sensing Team is responsible for integrating the scientific use of Earth-observation assets onboard the ISS, which consist of externally mounted sensors and crew photography capabilities. This team facilitates collaboration on remote sensing and participates in research with academic organizations and other Government agencies, not only in conjunction with ISS science, but also for planetary exploration and regional environmental/geological studies. Human exploration science focuses on science strategies for future human exploration missions to the Moon, Mars, asteroids, and beyond. This function provides communication and coordination between the science community and mission planners. ARES scientists support the operation of robotic missions (i.e., Mars Exploration Rovers and the Mars Science Laboratory), contribute to the interpretation of returned mission data, and translate robotic mission technologies and techniques to human spaceflight.

Calhoun, Tracy A.↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗