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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

Some preliminary indications were provided as to the relative merits of actual TM data versus MSS data for land cover mapping related applications. Three analyses were designed which had sensitivity to the differences in spectral, spatial and radiometric parameters between the TM and MSS. In the water body analysis, a primarily spatially related test, the detectability of small uniform targets was examined. The principal components analysis, an examination of the inherent dimensionality of the data, was more spectrally and radiometrically related. The spectral clustering analysis, also heavily spectrally and radiometrically influenced, provided information on the types of targets separable on TM versus MSS data. These analyses were to be conducted with simultaneously collected LANDSAT-4 complete TM (7 band) and MSS (4 band) data. In actuality, 4-band TM data, and archived LANDSAT-2 MSS data of the same area were used.

Markham, B. L.↗
NASA NTRS1990

Pattern recognition of clouds and ice in polar regions

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

Welch, R. M.↗
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
NASA NTRS2004

Automated Analysis, Classification, and Display of Waveforms

A computer program partly automates the analysis, classification, and display of waveforms represented by digital samples. In the original application for which the program was developed, the raw waveform data to be analyzed by the program are acquired from space-shuttle auxiliary power units (APUs) at a sampling rate of 100 Hz. The program could also be modified for application to other waveforms -- for example, electrocardiograms. The program begins by performing principal-component analysis (PCA) of 50 normal-mode APU waveforms. Each waveform is segmented. A covariance matrix is formed by use of the segmented waveforms. Three eigenvectors corresponding to three principal components are calculated. To generate features, each waveform is then projected onto the eigenvectors. These features are displayed on a three-dimensional diagram, facilitating the visualization of the trend of APU operations.

Kwan, Chiman↗
NASA NTRS2002

Data Analysis for the SOLIS Vector Spectromagnetograph

The National Solar Observatory's SOLIS Vector Spectromagnetograph (VSM), which will produce three or more full-disk maps of the Sun's photospheric vector magnetic field every day for at least one solar magnetic cycle, is in the final stages of assembly. Initial observations, including cross-calibration with the current NASA/NSO spectromagnetograph (SPM) will soon be carried out at a test site in Tucson. This paper discusses data analysis techniques for reducing the raw data, calculation of line-of-sight magnetograms and both quick-look and high-precision inference of vector fields from Stokes spectral profiles. Existing SPM algorithms, suitably modified to accomodate the cameras, scanning pattern, and polarization calibration optics for the VSM, will be used to "clean" the raw data and to process line-of-sight, magnetograms. A recent. version of the High Altitude Observatory Milne-Eddington (HAO-ME) inversion code (Skumanich and Lites; 1987, 11)J 322, p. 473) will he used for high-precision vector fields since the algorithm has been extensively tested, is well understood, and is fast enough to complete data analysis within 24 hours of data acquisition. The simplified inversion algorithm of Auer, Heasley. arid House (1977, Sol. Phys. 55, p. 47) forms the initial guess for this version of the HAO-ME code and will be used for quick-look vector analysis of VSM data since its performance on simulated Stokes profiles is better than other candidate methods. Improvements (e.g., principal components analysis or neural networks) are under consideration and will be straightforward to implement. However, current resources are sufficient to store the original Stokes profiles only long enough for high-precision analysis. Retrospective reduction of Stokes data with improved methods will not be possible, and modifications will only be introduced when the advantages of doing so are compelling enough to justify discontinuity in the long-term data stream.

Jones, Harrison P.↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗
NASA NTRS2013

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

Roberts, Yolanda, L.↗
NASA NTRS2001

Rotation of EOFs by the Independent Component Analysis: Towards A Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Aires, Filipe↗
NASA NTRS2000

Rotation of EOFs by the Independent Component Analysis: Towards a Solution of the Mixing Problem in the Decomposition of Geophysical Time Series

The Independent Component Analysis is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components, a stronger constraint that uses higher-order statistics, instead of the classical decorrelation, a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e. exploratory approach). We demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis, the Independent Component Analysis performs a rotation of the classical PCA (or EOF) solution. This rotation uses no localization criterion like other Rotation Techniques (RT), only the global generalization of decorrelation by statistical independence is used. This rotation of the PCA solution seems to be able to solve the tendency of PCA to mix several physical phenomena, even when the signal is just their linear sum.

Aires, Filipe↗
NASA NTRS1991

Humidity profiles over the ocean

The variabilities of atmospheric humidity profile over oceans from daily to interannual time scales were examined using 9 years of daily and semidaily radiosonde soundings at island stations extending from the Arctic to the South Pacific. The relative humidity profiles were found to have considerable temporal and geographic variabilities, contrary to the prevalent assumption. Principal component analysis on the profiles of specific humidity were used to examine the applicability of a relation between the surface-level humidity and the integrated water vapor; this relation has been used to estimate large-scale evaporation from satellite data. The first principal component was found to correlate almost perfectly with the integrated water vapor. The fractional variance represented by this mode increases with increasing period. It reaches approximately 90 percent at two weeks and decreases sharply, below one week, down to approximately 60 percent at the daily period. At low frequencies, the integrated water vapor appeared to be an adequate estimator of the humidity profile and the surface-level humidity. At periods shorter than a week, more than one independent estimator is needed.

Liu, W. T.↗
NASA NTRS1990

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Park, Steve↗
NASA NTRS2011

Jovian Chromophore Characteristics from Multispectral HST Images

The chromophores responsible for coloring the jovian atmosphere are embedded within Jupiter's vertical aerosol structure. Sunlight propagates through this vertical distribution of aerosol particles, whose colors are defined by omega-bar (sub 0)(lambda), and we remotely observe the culmination of the radiative transfer as I/F(lambda). In this study, we employed a radiative transfer code to retrieve omega-bar (sub 0)(lambda) for particles in Jupiter's tropospheric haze at seven wavelengths in the near-UV and visible regimes. The data consisted of images of the 2008 passage of Oval BA to the south of the Great Red Spot obtained by the Wide Field Planetary Camera 2 on-board the Hubble Space Telescope. We present derived particle colors for locations that were selected from 14 weather regions, which spanned a large range of observed colors. All omega-bar (sub 0)(lambda) curves were absorbing in the blue, and omega-bar (sub 0)(lambda) increased monotonically to approximately unity as wavelength increased. We found accurate fits to all omega-bar (sub 0)(lambda) curves using an empirically derived functional form: omega-bar (sub 0)(lambda) = 1 A exp(-B lambda). The best-fit parameters for the mean omega-bar (sub 0)(lambda) curve were A = 25.4 and B = 0.0149 for lambda in units of nm. We performed a principal component analysis (PCA) on our omega-bar (sub 0)(lambda) results and found that one or two independent chromophores were sufficient to produce the variations in omega-bar (sub 0)(lambda). A PCA of I/F(lambda) for the same jovian locations resulted in principal components (PCs) with roughly the same variances as the omega-bar (sub 0)(lambda) PCA, but they did not result in a one-to-one mapping of PC amplitudes between the omega-bar (sub 0)(lambda) PCA and I/F(lambda) PCA. We suggest that statistical analyses performed on I/ F(lambda) image cubes have limited applicability to the characterization of chromophores in the jovian atmosphere due to the sensitivity of 1/ F(lambda) to horizontal variations in the vertical aerosol distribution.

Strycker, Paul D.↗
NASA NTRS1982

Classification of surface units in the equatorial region of Mars based on Viking Orbiter color, albedo, and thermal data

Clusters corresponding to mappable surface units are sought in Viking Orbiter color, albedo, and thermal inertia data for the equatorial region of Mars. A principal components analysis indicated that 84% of the variance within the data for this region can be carried along two vector directions which typify the dominant trend of Martian surface materials. These dominant trends were deemphasized by stretching the data from a five-dimensional elliptical swarm into a hypersphere, through the use of principal component techniques. The decorrelated data were then plotted in a triangle diagram with red/violet, albedo and thermal inertia apices to facilitate inherent cluster discrimination. As many as eight clusters can be identified, with important mixing between them. The three major clusters consist of red and grey material extremes, along with intermediate value materials.

Arvidson, R. E.↗
NASA NTRS2000

Independent Component Analysis of Textures

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Manduchi, Roberto↗
NASA NTRS2001

Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP)

The relative effectiveness of average-well-color-development-normalized single-point absorbance readings (AWCD) vs the kinetic parameters mu(m), lambda, A, and integral (AREA) of the modified Gompertz equation fit to the color development curve resulting from reduction of a redox sensitive dye from microbial respiration of 95 separate sole carbon sources in microplate wells was compared for a dilution series of rhizosphere samples from hydroponically grown wheat and potato ranging in inoculum densities of 1 x 10(4)-4 x 10(6) cells ml-1. Patterns generated with each parameter were analyzed using principal component analysis (PCA) and discriminant function analysis (DFA) to test relative resolving power. Samples of equivalent cell density (undiluted samples) were correctly classified by rhizosphere type for all parameters based on DFA analysis of the first five PC scores. Analysis of undiluted and 1:4 diluted samples resulted in misclassification of at least two of the wheat samples for all parameters except the AWCD normalized (0.50 abs. units) data, and analysis of undiluted, 1:4, and 1:16 diluted samples resulted in misclassification for all parameter types. Ordination of samples along the first principal component (PC) was correlated to inoculum density in analyses performed on all of the kinetic parameters, but no such influence was seen for AWCD-derived results. The carbon sources responsible for classification differed among the variable types with the exception of AREA and A, which were strongly correlated. These results indicate that the use of kinetic parameters for pattern analysis in CLPP may provide some additional information, but only if the influence of inoculum density is carefully considered. c2001 Elsevier Science Ltd. All rights reserved.

NASA Center KSC↗
NASA NTRS2007

Anthropometric Accommodation in Space Suit Design

Design requirements for next generation hardware are in process at NASA. Anthropometry requirements are given in terms of minimum and maximum sizes for critical dimensions that hardware must accommodate. These dimensions drive vehicle design and suit design, and implicitly have an effect on crew selection and participation. At this stage in the process, stakeholders such as cockpit and suit designers were asked to provide lists of dimensions that will be critical for their design. In addition, they were asked to provide technically feasible minimum and maximum ranges for these dimensions. Using an adjusted 1988 Anthropometric Survey of U.S. Army (ANSUR) database to represent a future astronaut population, the accommodation ranges provided by the suit critical dimensions were calculated. This project involved participation from the Anthropometry and Biomechanics facility (ABF) as well as suit designers, with suit designers providing expertise about feasible hardware dimensions and the ABF providing accommodation analysis. The initial analysis provided the suit design team with the accommodation levels associated with the critical dimensions provided early in the study. Additional outcomes will include a comparison of principal components analysis as an alternate method for anthropometric analysis.

Rajulu, Sudhakar↗
NASA NTRS1999

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

meteorites Amino acids Multivariate Analysis↗
NASA NTRS1992

Tropical intraseasonal oscillation and its prediction by the NMC operational model

Results are presented of an investigation of the tropical intraseasonal oscillation (ISO) and its impact on the extended-range forecast in the NMC operational model during Phase II (14 December 1986-31 March 1987) of the Dynamical Extended Range Forecast. Based on principal component analysis of the velocity potential and streamfunction, evidence was found of tropical-extratropical interaction associated with the ISO. The NMC model possess significant forecast skills for the principal streamfunction and velocity potential modes up to the first ten days. Results of the error growth analysis suggest that the principal modes of velocity potential have large errors comparable to the model random errors. By comparison, the initial errors in the streamfunction are much smaller. The error growth for both tropical and extratropical modes are found to be significantly suppressed during periods of strong ISO relative to periods of weak ISO. The increase in extratropical forecast skill is likely due to (1) the model's ability to better capture ISO signals in the tropics and (2) the increased coupling between the tropics and extratropics during periods of strong ISO.

Lau, K.-M.↗