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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Learning receptor positions from imperfectly known motions

An algorithm is described for learning image interpolation functions for sensor arrays whose sensor positions are somewhat disordered. The learning is based on failures of translation invariance, so it does not require knowledge of the images being presented to the visual system. Previously reported implementations of the method assumed the visual system to have precise knowledge of the translations. It is demonstrated that translation estimates computed from the imperfectly interpolated images can have enough accuracy to allow the learning process to converge to a correct interpolation.

Ahumada, Albert J., Jr.↗
NASA NTRS1977

A survey of SAR image-formation processing for earth resources applications

Currently there is considerable interest in active microwave sensors for earth resources applications, such as the SEASAT-A radar. However, to obtain spatial resolutions comparable to optical sensors at radar frequencies, sophisticated image formation processing techniques must be applied to the raw data. Processing requirements for non-coherent optical and coherent radar imaging systems are compared. The image formation processing requirements for synthetic aperture radar (SAR) systems are discussed. Both optical and digital techniques are addressed, and examples of hardware and imagery for each processing technique are presented.

Bayma, R. W.↗
NASA NTRS1977

A survey of SAR image-formation processing for earth resources applications

Currently there is considerable interest in active microwave sensors for earth resources applications. A particular example is the Seasat-A radar. However, to obtain spatial resolutions comparable to optical sensors at radar frequencies, sophisticated image formation processing techniques must be applied to the raw data. This paper briefly compares processing requirements for non-coherent optical and coherent radar imaging systems, and then discusses the image formation processing requirements for synthetic aperture radar (SAR) systems. Both optical and digital techniques are addressed, and examples of hardware and imagery for each processing technique are presented.

Bayma, R. W.↗
NASA NTRS1988

The Multichannel Ocean Color Sensor (MOCS)

The MOCS is an imaging spectroradiometer with a sensitivity that matches the light upwelling from open bodies of water. Twenty contiguous spectral channels in the visible are recorded in a line scanning mode from an aircraft. The system also supports a thermal infrared radiometer and will accept input from a position location system. The sensor in the MOCS system is an image dissector tube. A spectral curvature algorithm was developed. No atmospheric correction to the data is needed for low flying aircraft gathering chlorophyll data using this algorithm. Although the MOCS is now about 15 years old, because its response has been so stable, its data recording system was upgraded to the system used by the Aircraft Oceanographic Lidar where possible. The MOCS has been a reliable instrument for recording ocean color from low flying aircraft. As NASA enters the next period of ocean color investigations from space, such a device can play an important role in calibration of the space instrument, as well as provide support for programs developing the next generation of spaceborne devices.

Oberholtzer, J. D.↗
NASA NTRSDate not supplied

Deriving Broadband Radiances from Geostationary Satellite Visible Radiance Channels for Use in the CERES SYN1deg Product

The NASA CERES project was designed to monitor the Earth’s radiation budget, both at the regional and global scale. The CERES SSF1deg single satellite product provides TOA flux observations from Aqua, Terra, NPP, and NOAA-20. The SYN1deg product incorporates observations from Terra, Aqua, and geostationary (GEO) satellites. The GEO observations are included in the SYN1deg product to infer the regional diurnal cycle between CERES observations. GEO satellites throughout the CERES record have different narrowband spectral response functions and do not directly observe radiances over the broadband spectra of CERES instruments. Hence the emphasis of this study is improving the process of deriving broadband radiances from the myriad GEO visible channel observations. We present a new method for converting the narrowband radiances observed from GEO operational satellites. This study relies on radiative transfer model hyper-spectral radiances for many cloud and surface conditions under all angular conditions observed by GEO sensors. First, the hyper-spectral radiances from a radiative transfer model must be convolved with the spectral response function of the visible imager for each GEO sensor in the CERES record. For each angular bin and surface type, the clear-sky and multiple optical depth and phase cloudy-sky narrowband and broadband reflectance pairs are linearly regressed. This study found that, no matter how the scene conditions are stratified, for example the all-sky, clear-sky, ice or water clouds, they all provide similar linear regressions. This includes ocean glint conditions, where the clear-sky reflectance exceeds cloudy conditions and yet the relationship was found to be linear in the given angular bin. This infers that no GEO retrieved cloud information is necessary in the narrowband to broadband conversion process, which relies only on visible channel reflectance. Over land, most of the GEO visible channels do not observe the NIR vegetation reflectance. We intend to use land adjustment factors developed using the TOA reflectance difference between the real-time MODIS .86um and the .64um channel. We validate this approach by using coincident observational data between CERES and MODIS aboard Aqua and Terra as well as GEO and CERES observations .

CERES↗
NASA NTRS2018

Initial Pre-Launch Imaging and Spectral Characterization of Landsat 9 Thermal Infrared Sensor-2

The Thermal Infrared Sensor-2 (TIRS-2) scheduled to launch in December 2020 aboard Landsat 9 will continue Landsat's four decade-long legacy of providing moderate resolution thermal imagery from low earth orbit (at 705 km) for environmental applications. Like the Thermal Infrared Sensor aboard Landsat 8, it is a pushbroom sensor with a cross-track field of view of 15 and provides two spectral channels at 10.8 and 12 micrometers To ensure radiometric, spatial, and spectral performance, a comprehensive pre-launch testing program is being conducted at NASA Goddard Space Flight Center at the component, subsystem, and instrument level. This effort will focus on the results from the subsystem level testing to assess TIRS-2 imaging performance including focus, spatial performance, and stray light rejection. It is also used to provide a preliminary assessment of spectral performance. The TIRS-2 subsystem is placed in a thermal vacuum chamber with the calibration ground support equipment, which provides a flexible blackbody illumination source and optics to assess imaging performance. Spectral performance is tested using a spectral response test setup with its own illumination source outside the chamber that propagates through the calibration ground support equipment in an optical configuration designed for this purpose. The results show that TIRS-2 performance is expected to meet all of its performance requirements with few waivers and deviations.

Landsat 9↗
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 NTRS1981

An image-data-compression algorithm

Cluster Compression Algorithm (CCA) preprocesses Landsat image data immediately following satellite data sensor (receiver). Data are reduced by extracting pertinent image features and compressing this result into concise format for transmission to ground station. This results in narrower transmission bandwidth, increased data-communication efficiency, and reduced computer time in reconstructing and analyzing image. Similar technique could be applied to other types of recorded data to cut costs of transmitting, storing, distributing, and interpreting complex information.

Hilbert, E. E.↗
NASA NTRS1983

Impact of LANDSAT MSS sensor differences on change detection analysis

Differences in overall sensor response, due to both factors within the sensor system, and external factors such as atmospheric effects, must be dealt with before change detection techniques using image differencing can be successfully implemented. The approach used to be coregister data to a base, followed by between image scatterogram generation and evaluation, and image differencing. Data from LANDSAT 2 and 3 were registered to LANDSAT 4 data. The area was limited to a small protion of the scene near the City of Santa Cruz because this was the only area cloud and haze free on all the images. Visual examination of each image, as well as the between image scatterograms, provide information on radiometric characteristics of each sensor. The image differencing, after normalizing for radiometric differences, provides a visual depiction of geometric distortions.

Likens, W.↗
NASA NTRS1987

Program for Analysis and Enhancement of Images

Land Analysis System (LAS) is collection of image-analysis computer programs designed to manipulate and analyze multispectral image data. Provides user with functions ingesting various sensor data, radiometric and geometric corrections, image registration, training site selection, supervised and unsupervised classification, Fourier domain filtering, and image enhancement. Sufficiently modular and includes extensive library of subroutines to permit inclusion of new algorithmic programs. Commercial package International Mathematical & Statistical Library (IMSL) required for full implementation of LAS. Written in VAX FORTRAN 77, C, and Macro assembler for DEC VAX operating under VMS 4.0.

Lu, Yun-Chi↗
NASA NTRS2014

How Do A-train Sensors Intercompare in the Retrieval of Above-cloud Aerosol Optical Depth? A Case Study-based Assessment

We intercompare the above-cloud aerosol optical depth (ACAOD) of biomass burning plumes retrieved from A-train sensors, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Polarization and Directionality of Earth Reflectances (POLDER), and Ozone Monitoring Instrument (OMI). These sensors have shown independent capabilities to retrieve aerosol loading above marine boundary layer clouds-a kind of situation often found over the southeast Atlantic Ocean during dry burning season. A systematic comparison reveals that all passive sensors and CALIOP-based research methods derive comparable ACAOD with differences mostly within 0.2 over homogeneous cloud fields. The 532 nm ACAOD retrieved by CALIOP operational algorithm is underestimated. The retrieved 1064 nm AOD however shows closer agreement with passive sensors. Given the different types of measurements processed with different algorithms, the reported close agreement between them is encouraging. Due to unavailability of direct measurements above cloud, the validation of satellite-based ACAOD remains an open challenge. The intersatellite comparison however can be useful for the relative evaluation and consistency check

Aerosol↗
NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗
NASA NTRS1986

Design and performance of 4 x 5120-element visible and 2 x 2560-element shortwave infrared multispectral focal planes

Two solid-state sensors for use in remote sensing instruments operating in the pushbroom mode are examined. The design and characteristics of the visible/near-infrared (VIS/NIR) device and the short-wavelength infrared (SWIR) device are described. The VIS/NIR is a CCD imager with four parallel sensor lines, each 1024 pixel long; the chip design and filter system of the VIS/NIR are studied. The performance of the VIS/NIR sensor with mask and its system performance are measured. The SWIR is a dual-band line imager consisting of palladium silicide Schottky-barrier detectors coupled to CCD multiplexers; the performance of the device is analyzed. The substrate materials and layout designs used to assemble the 4 x 5120-element VIS/NIR array and the 2 x 2560-element SWIR array are discussed, and the planarity of the butted arrays are verified using a profilometer. The optical and electrical characteristics, and the placement and butting accuracy of the arrays are evaluated. It is noted that the arrays met or exceed their expected performance.

Tower, J. R.↗
NASA NTRS2016

Up Periscope! Designing a New Perceptual Metric for Imaging System Performance

Modern electronic imaging systems include optics, sensors, sampling, noise, processing, compression, transmission and display elements, and are viewed by the human eye. Many of these elements cannot be assessed by traditional imaging system metrics such as the MTF. More complex metrics such as NVTherm do address these elements, but do so largely through parametric adjustment of an MTF-like metric. The parameters are adjusted through subjective testing of human observers identifying specific targets in a set of standard images. We have designed a new metric that is based on a model of human visual pattern classification. In contrast to previous metrics, ours simulates the human observer identifying the standard targets. One application of this metric is to quantify performance of modern electronic periscope systems on submarines.

imaging↗
NASA NTRS1991

Parameter trade-offs for imaging spectroscopy systems

With the advent of the EOS era and of configurable sensors, users of these instruments are faced with the twin problems of specifying data acquisition parameters and extracting desired information from the voluminous data. An application of a system model is made to explore system parameter trade-offs for a model sensor based on the High Resolution Imaging Spectrometer. Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. Using a model scene based on typical agricultural reflectance and atmospheric conditions, the atmosphere and sensor are seen to have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is seen to degrade system performance, especially in the spectral bands below 1 micron. Accurate pixel-to-pixel relative radiometric calibration and the use of the Image Motion Compensation option are seen to improve classification accuracy, especially at high solar zenith angles. Feature sets chosen from characteristics of the scene performed best overall, but ones chosen based on signal-to-noise ratios were seen to be more robust.

Kerekes, John P.↗
NASA NTRS2016

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Huang, Jingfeng↗
DOE OSTI2020

Material identification system

A method and apparatus for identifying a material in an object. An image of the object generated from energy passing through the object is obtained by a computer system. The computer system estimates attenuations for pixels in a sensor system from the image of the object to form estimated attenuations. The estimated attenuations represent a loss of the energy that occurs from the energy passing through the object. The computer system also identifies the material in the object using the estimated attenuations and known attenuation information for identifying the material in the object.

Jimenez, Jr., Edward Steven↗
NASA NTRSDate not supplied

The Dark Target aerosol retrieval algorithm applied to Low Earth Orbit and GEOstationary imagers: progress towards an integrated LEO-GEO view of global aerosol

The relatively simple dark-target (DT) aerosol retrieval algorithm provides products of spectral aerosol optical depth (AOD) from measurements of multi-spectral reflectance in visible, near-infrared and shortwave infrared wavelength bands. Originally developed for Moderate-resolution Imaging Spectroradiometer (MODIS aboard Terra and Aqua) in Low-Earth Orbit (LEO), DT has been ported to Visible Infrared Imaging Suite (VIIRS aboard Suomi-NPP and NOAA-20, also in LEO), to enhanced-MODIS Airborne Simulator (eMAS, on an airborne platform), and now to sensors in GEOstationary orbit (Advanced Himawari Imager - AHI aboard Himawari-8 and Advanced Baseline Imagers – ABI aboard GOES-16 and 17). Together, these new datasets not only extend upon the 20+ year MODIS aerosol record, but also expand the temporal sampling and/or spatial resolution. Between July and October of 2019, NASA participated in two field experiments on opposite sides of the globe. These included FIREX-AQ which focused on fire and smoke in the Western U.S., and then CAMP2EX which targeted aerosol/cloud interactions around the Philippines. We have performed DT aerosol retrievals on all images from all sensors during these three months, validated against ground observations from stationary and mobile sunphotometer sites, and have begun to develop a synergy that represents semi-global observations every half hour. The resulting aerosol products are being used as context and for model assimilation, thus providing the framework for more complete characterization of global aerosol transport and lifecycle. Here, we report on progress, as well as remaining challenges such as data management, computer processing, and accounting for differences between GEO and LEO observation geometry and surface reflectance parameterization.

dark target↗