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At least 937 records · Page 52

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NASA NTRS1979

On-board multispectral classification study

The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.

Ewalt, D.↗

NASA NTRS1980

Study of the atmospheric effects on the radiation detected by the sensor aboard orbiting platforms (ERTS/LANDSAT)

The author has identified the following significant results. Multispectral scanner data for Brasilia was corrected for atmospheric interference using the LOWTRAN-3 computer program and the analytical solution of the radiative transfer equation. This improved the contrast between two natural targets and the corrected images of two different dates were more similar than the original ones. Corrected images of MSS data for Ribeirao Preto gave a classification accuracy for sugar cane about 10% higher as compared to the original images.

Dejesusparada, N.↗

NASA NTRS1981

Texture measurements from Seasat - SAR images for urban land use interpretation

Different grey tones in Seasat Synthetic Aperture Radar (SAR) images for the same type of urban land cover considerably impair the possibilities of establishing automatic classification procedures for these types of data. Since the orientation of the main features like street patterns and buildings with respect to the azimuth angle of the radar antenna is the crucial factor for the observed differences in grey tone, prior information on these elements and special processing of the data would be required to eliminate this effect. Another approach suggested in this paper is to make use of the textural information in the image rather than of its grey tone. For different study sites within the Los Angeles urbanized area texture measures could be derived which result in characteristic values for specific types of land cover and are largely independent of the azimuth angle effect. At the same time the results for the study area indicate an improvement of the overall separability for the different land cover types included in the analysis.

Fasler, F.↗

NASA NTRS1981

Sampling Landsat classifications for crop area estimation

An investigation was conducted to evaluate the effect of several sampling alternatives on the accuracy of crop area estimates made from classification of Landsat Multispectral Scanner (MSS) data. The specific objective was to assess the precision and the bias associated with alternative sampling schemes involving different numbers of several sampling unit sizes. The estimates achieved using the 5 by 6 nm segments were found to have the least precision of any sampling scheme tested. The estimates become more precise as the segment size decreases and more segments are taken. The precision of the 5 by 6 nm segments was significantly less than that of the pixel samples. None of the sampling schemes was significantly biased on the average, and none of the average estimates differed significantly from the population parameter. The maximum absolute deviation, however, was directly related to sampling unit size and should be considered in selection of a sampling unit.

Hixson, M. M.↗

NASA NTRS1981

Application of scanning microdensitometer data in selected plant science case studies

This paper treats a representative sample of projects aimed at vegetation mapping and assessment via analysis of both digital photographic and Landsat data projects and illustrates the potential for using scanning microdensitometric data extracted from color infrared photographs in the following applications: freshwater wetlands mapping, tree type mapping, and yield vs. reflectance modeling in corn fertilization experiments. These case studies are presented to illustrate the general applicability of scanning microdensitometer data in the contexts of image classification and enhancement as well as quantitative modeling of ground parameters.

Lillesand, T. M.↗

NASA NTRS1982

Evaluation of temporal registration of Landsat scenes

The registration of Landsat images is important for multitemporal classification and for detecting change. Landsat data are now rectified to a ground coordinate system during preprocessing, hence scenes obtained over the same area are registered. The machine responsible for preprocessing the Landsat multispectral scanner data is the master data processor (MDP). This paper describes the rectification approach taken by the MDP, reviews the accuracy standards of the resultant product, and provides an assessment of the accuracy of the scene to scene registration of two Landsat images.

Nelson, R.↗

NASA NTRS1982

Vegetation survey in Amazonia using LANDSAT data

Automatic Image-100 analysis of LANDSAT data was performed using the MAXVER classification algorithm. In the pilot area, four vegetation units were mapped automatically in addition to the areas occupied for agricultural activities. The Image-100 classified results together with a soil map and information from RADAR images, permitted the establishment of the final legend with six classes: semi-deciduous tropical forest; low land evergreen tropical forest; secondary vegetation; tropical forest of humid areas, predominant pastureland and flood plains. Two water types were identified based on their sediments indicating different geological and geomorphological aspects.

Parada, N. D. J.↗

NASA NTRS1981

Low-cost digital image processing on a university mainframe computer

The advantages and limitations of using university mainframe computers in digital image processing instruction are listed. Aspects to be considered when designing software for this purpose include not only two general audience, but also the capabilities of the system regarding the size of the image/subimage, preprocessing and enhancement functions, geometric correction and registration techniques; classification strategy, classification algorithm, multitemporal analysis, and ancilliary data and geographic information systems. The user/software/hardware interaction as well as acquisition and operating costs must also be considered.

Williams, T. H. L.↗

NASA NTRS1983

Integration of environmental and spectral data for sunflower stress determination

Stress in sunflowers was assessed in western and northwestern Minnesota. Weekly ground observations (acquired in 1980 and 1981) were analyzed in concert with large scale aerial photography and concurrent LANDSAT data. Using multidate supervised and unsupervised classification procedures, it was found that all crops grown in association with sunflowers in the study area are spectrally separable from one another. Under conditions of extreme drought, severely stressed plants were differentiable from those not severely stressed, but between-crop separation was not possible. Initial regression analyses to estimate sunflower seed yield showed a sensitivity to environmental stress during the flowering and seed development stages. One of the most important biological factors related to sunflower production in the Red River Valley area was found to be the extent and severity of insect infestations.

Lillesand, T.↗

NASA NTRS1983

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Logan, T. L.↗

NASA NTRS1984

Assessment of LANDSAT for rangeland mapping, Rush Valley, Utah

The feasibility of using LANDSAT MSS (multispectral scanner) data to identify and map cover types for rangeland, and to determine comparative condition of the ecotypes was assessed. A supporting objective is to assess the utility of various forms of aerial photography in the process. If rangelands can be efficiently mapped with Landsat data, as supported by appropriate aerial photography and field data, then uniform standards of cover classification and condition may be applied across the rangelands of the state. Further, a foundation may be established for long-term monitoring of range trend, using the same satellite system over time.

Ridd, M. K.↗

NASA NTRS1987

Correlation between aircraft MSS and lidar remotely sensed data on a forested wetland

Inland wetland in a portion of the Savannah River swamp forest were mapped with an overall accuracy of 88.5 percent on April 26,l985 using high resolution aircraft Daedalus AADS-1268 MSS data. In addition, data were acquired using a NASA sensor system flown along two flight lines over the Steel Creek Delta. The data were significantly correlated with in situ tree height measurements. The data were registered to the wetland classification map and correlated. Statistical analyses demonstrated that the laser derived canopy height information was significantly associated with the Steel Creek Delta wetland classes encountered along the transect (an F-value of 58.46 at the 0.0001 level of confidence). The relationship between vegetation height and vegetation type was then used to produce a three-dimensional model of the landscape which can be of value when computing biomass or canopy density in this forested wetland environment.

Jensen, John R.↗

NASA NTRS1991

Near-Earth asteroids: Observer alert network and database analysis

The Planetary Science Institute (PSI) was funded by SERCulpr to develop a communication network to alert observers of newly discovered near-earth asteroids (NEA's). This network is intended to encourage observers to obtain physical observations of NEA's, which are needed in order to characterize and assess the resource potential of these bodies. This network was declared operational in October 1990 via an announcement to the asteroid observing community. The PSI is also supported to develop the Near-Earth Asteroid Database (NEAD), a comprehensive database of physical and dynamical data on NEA's. In the past year, the database was updated on newly discovered NEA's during 1990, and new data on radar observations and dynamical classifications were added.

Davis, Donald R.↗

NASA NTRS2001

Transport of Energetic Ions in the Ring Current During Geomagnetic Storms

In the final year (plus no-cost extentions) of this grant, we have: Used the particle tracing code to perform a systematic study of the expected energy spectra over the full range of local times in the ring current using a variety of electric and magnetic field models. Shown that the Weimer electric field is superior to the Volland-Stern electric field in reproducing the observed energy spectra on the AMPTE CCE spacecraft. Redone our analysis of the pitch angle spectra of energetic ions during storms in the magnetosphere, using a larger data set, and a more reliable classification technique.

Kistler, Lynn M.↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Image Processing and Earth Observations

The titles in this section include: 1) Expansion in Geographic Information Services for PIGWAD; 2) Modernization of the Integrated Software for Imagers and Spectrometers; 3) Science-based Region-of-Interest Image Compression; 4) Topographic Analysis with a Stereo Matching Tool Kit; 5) Central Avra Valley Storage and Recovery Project (CAVSARP) Site, Tucson, Arizona: Floodwater and Soil Moisture Investigations with Extraterrestrial Applications; 6) ASE Floodwater Classifier Development for EO-1 HYPERION Imagery; 7) Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004; 8) Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data; 9) Long-Term Continental Areal Reduction Produced by Tectonic Processes.

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NASA NTRS2005

Martian Impact Craters as Revealed by MGS and Odyssey

A variety of ejecta and interior morphologies were revealed for martian impact craters by Viking imagery. Numerous studies have classified these ejecta and interior morphologies and looked at how these morphologies correlate with crater diameter, latitude, terrain, and elevation [1, 2, 3, 4]. Many of these features, particularly the layered (fluidized) ejecta morphologies and central pits, have been proposed to result when the crater formed in target material containing high concentrations of volatiles. The Catalog of Large Martian Impact Craters was originally derived from the Viking 1:2,000,000 photomosaics and contains information on 42,283 impact craters 5-km diameter distributed across the entire martian surface. The information in this Catalog has been used to study the distributions of craters displaying specific ejecta and interior morphologies in an attempt to understand the environmental conditions which give rise to these features and to estimate the areal and vertical extents of subsurface volatile reservoirs [4, 5]. The Catalog is currently undergoing revision utilizing Mars Global Surveyor (MGS) and Mars Odyssey data [6]. The higher resolution multispectral imagery is resulting in numerous revisions to the original classifications and the addition of new elemental, thermophysical, and topographic data is allowing new insights into the environmental conditions under which these features form. A few of the new results from analysis of data in the revised Catalog are discussed below.

Barlow, N. G.↗

NASA NTRS2009

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.

Gay, Gregory↗

NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗