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At least 235 records · Page 13

Variables in automatic classification over extended remote sensing test sites.

Four major causes of variation in response of multispectral scanner data were examined utilizing data from two large test sites. In one case, data throughout a 70-mile flightline was automatically classified with a high degree of accuracy (98%), utilizing training samples from a single small segment of the data. In the second case, spectral data for wheat and other cover types were calibrated and utilized to train the computer, resulting in data up to 90 miles away being classified with an acceptable degree of accuracy (91%), although significant changes in solar illumination and ground cover conditions existed.

Hoffer, R. M.↗

A self-documenting source-independent data format for computer processing of tensor time series

The UCLA Space Science Group has developed a fixed format intermediate data set called a block data set, which is designed to hold multiple segments of multicomponent sampled data series. The format is sufficiently general so that tensor functions of one or more independent variables can be stored in the form of virtual data. This makes it possible for the unit data records of the block data set to be arrays of a single dependent variable rather than discrete samples. The format is self-documenting with parameter, label and header records completely characterizing the contents of the file. The block data set has been applied to the filing of satellite data (of ATS-6 among others).

Mcpherron, R. L.↗

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗

Signature extension methods in crop area estimation

The Procedure B multispectral processing system is both multisegment and multistratum. It uses data from several LACIE-sized segments together and makes a proportion estimate for the entire group of segments as well as for the individual segments. In the clustering of data features, Procedure B produces multiple classes or strata rather than just two strata (as in Procedure 1), and performs stratified sampling on each of these mutliple strata in order to make a proportion estimate. Tests results for the components and for the overall performance of Procedure B are presented, and conclusions that can be drawn from these tests are discussed. The rationale for signature extension for crop area estimation is summarized.

Kauth, R. J.↗

Gravity Wave Energetics Determined From Coincident Space-Based and Ground-Based Observations of Airglow Emissions

Significant progress was made toward the goals of this proposal in a number of areas during the covered period. Section 5.1 contains a copy of the originally proposed schedule. The tasks listed below have been accomplished: (1) Construction of space-based observing geometry gravity wave model. This model has been described in detail in the paper accompanying this report (Section 5.2). It can simulate the observing geometry of both ground-based, and orbital instruments allowing comparisons to be made between them. (2) Comparisons of relative emission intensity, temperatures, and Krassovsky's ratio for space- and ground-based observing geometries. These quantities are used in gravity wave literature to describe the effects of the waves on the airglow. (3) Rejection of Bates [1992], and Copeland [1994] chemistries for gravity wave modeling purposes. Excessive 02(A(sup 13)(Delta)) production led to overproduction of O2(b(sup 1)(Sigma)), the state responsible for the emission of O2. Atmospheric band. Attempts were made to correct for this behavior, but could not adequately compensate for this. (4) Rejection of MSX dataset due to lack of coincident data, and resolution necessary to characterize the waves. A careful search to identify coincident data revealed only four instances, with only one of those providing usable data. Two high latitude overpasses and were contaminated by auroral emissions. Of the remaining two mid-latitude coincidences, one overflight was obscured by cloud, leaving only one ten minute segment of usable data. Aside from the statistical difficulties involved in comparing measurements taken in this short period, the instrument lacks the necessary resolution to determine the vertical wavelength of the gravity wave. This means that the wave cannot be uniquely characterized from space with this dataset. Since no observed wave can be uniquely identified, model comparisons are not possible.

Source record↗

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗

Telecommunications end-to-end systems monitoring on TOPEX/Poseidon: Tools and techniques

The TOPEX/Poseidon Project Satellite Performance Analysis Team's (SPAT) roles and responsibilities have grown to include functions that are typically performed by other teams on JPL Flight Projects. In particular, SPAT Telecommunication's role has expanded beyond the nominal function of monitoring, assessing, characterizing, and trending the spacecraft (S/C) RF/Telecom subsystem to one of End-to-End Information Systems (EEIS) monitoring. This has been accomplished by taking advantage of the spacecraft and ground data system structures and protocols. By processing both the received spacecraft telemetry minor frame ground generated CRC flags and NASCOM block poly error flags, bit error rates (BER) for each link segment can be determined. This provides the capability to characterize the separate link segments, determine science data recovery, and perform fault/anomaly detection and isolation. By monitoring and managing the links, TOPEX has successfully recovered approximately 99.9 percent of the science data with an integrity (BER) of better than 1 x 10(exp 8). This paper presents the algorithms used to process the above flags and the techniques used for EEIS monitoring.

Calanche, Bruno J.↗

Mind the Gap: Addressing Data Gaps and Assessing Noise Mismodeling in LISA

Due to the sheer complexity of the Laser Interferometer Space Antenna (LISA) space mission, data gaps arising from instrumental irregularities and/or scheduled maintenance are unavoidable. Focusing on merger-dominated massive black hole binary signals, we test the appropriateness of the Whittle-likelihood on gapped data in a variety of cases. From first principles, we derive the likelihood valid for gapped data in both the time and frequency domains. Cheap-to-evaluate proxies to p-p plots are derived based on a Fisher-based formalism, and verified through Bayesian techniques. Our tools allow to predict the altered variance in the parameter estimates that arises from noise mismodeling, as well as the information loss represented by the broadening of the posteriors. The result of noise mismodeling with gaps is sensitive to the characteristics of the noise model, with strong low-frequency (red) noise and strong high-frequency (blue) noise giving statistically significant fluctuations in recovered parameters. We demonstrate that the introduction of a tapering window reduces statistical inconsistency errors, at the cost of less precise parameter estimates. We also show that the assumption of independence between inter-gap segments appears to be a fair approximation even if the data set is inherently coherent. However, if one instead assumes fictitious correlations in the data stream, when the data segments are actually independent, then the resultant parameter recoveries could be inconsistent with the true parameters. The theoretical and numerical practices that are presented in this work could readily be incorporated into global-fit pipelines operating on gapped data.

LISA↗

AVIRIS study of Death Valley evaporite deposits using least-squares band-fitting methods

Minerals found in playa evaporite deposits reflect the chemically diverse origins of ground waters in arid regions. Recently, it was discovered that many playa minerals exhibit diagnostic visible and near-infrared (0.4-2.5 micron) absorption bands that provide a remote sensing basis for observing important compositional details of desert ground water systems. The study of such systems is relevant to understanding solute acquisition, transport, and fractionation processes that are active in the subsurface. Observations of playa evaporites may also be useful for monitoring the hydrologic response of desert basins to changing climatic conditions on regional and global scales. Ongoing work using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to map evaporite minerals in the Death Valley salt pan is described. The AVIRIS data point to differences in inflow water chemistry in different parts of the Death Valley playa system and have led to the discovery of at least two new North American mineral occurrences. Seven segments of AVIRIS data were acquired over Death Valley on 31 July 1990, and were calibrated to reflectance by using the spectrum of a uniform area of alluvium near the salt pan. The calibrated data were subsequently analyzed by using least-squares spectral band-fitting methods, first described by Clark and others. In the band-fitting procedure, AVIRIS spectra are fit compared over selected wavelength intervals to a series of library reference spectra. Output images showing the degree of fit, band depth, and fit times the band depth are generated for each reference spectrum. The reference spectra used in the study included laboratory data for 35 pure evaporite spectra extracted from the AVIRIS image cube. Additional details of the band-fitting technique are provided by Clark and others elsewhere in this volume.

Crowley, J. K.↗

Observations on TeV gamma rays from Geminga and PSR 0950+08

The Geminga (2 CG 195+04) which exhibits a periodicity with a period of 59 to 60 s in its emission of X-rays, GeV gamma rays and TeV gamma rays was studied. During the winter of 1984 to 1985, this object was observed to see if it emits TeV gamma rays with a periodicity approx 60 s. The observations were carried out at two different sites separated by 11 Km with the Ooty Atmospheric Cerenkov Array split into two parts. Data were collected during clear moonless nights for a total duration of 15.3 hours spread over 2 months. Since the first time derivative of period is believed to be large and uncertain. The total data are subdivided into segments of duration not more than 3 days each to steer clear of the effects of P in the phase analysis. If TeV gamma ray signals are seen in each of these segments, it is possible to derive P from observed data.

Bhat, P. N.↗

Identification of sea ice types in spaceborne synthetic aperture radar data

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Kwok, Ronald↗

Orbit-determination performance of Doppler data for interplanetary cruise trajectories. Part 2: 8.4-GHz performance and data-weighting strategies

A consider error covariance analysis was performed in order to investigate the orbit-determination performance attainable using two-way (coherent) 8.4-GHz (X-band) Doppler data for two segments of the planned Mars Observer trajectory. The analysis includes the effects of the current level of calibration errors in tropospheric delay, ionospheric delay, and station locations, with particular emphasis placed on assessing the performance of several candidate elevation-dependent data-weighting functions. One weighting function was found that yields good performance for a variety of tracking geometries. This weighting function is simple and robust; it reduces the danger of error that might exist if an analyst had to select one of several different weighting functions that are highly sensitive to the exact choice of parameters and to the tracking geometry. Orbit-determination accuracy improvements that may be obtained through the use of calibration data derived from Global Positioning System (GPS) satellites also were investigated, and can be as much as a factor of three in some components of the spacecraft state vector. Assuming that both station-location errors and troposphere calibration errors are reduced simultaneously, the recommended data-weighting function need not be changed when GPS calibrations are incorporated in the orbit-determination process.

Ulvestad, J. S.↗

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

4D-STEM↗

Accelerating shared file checkpoint with local burst buffers

A data management system and method for accelerating shared file checkpointing. Written application data is aggregated in an application data file created in a local burst buffer memory at a compute node, and an associated data mapping built index to maintain information related to the offsets into a shared file at which segments of the application data is to be stored in a parallel file system, and where in the buffer those segments are located. The node asynchronously transfers a data file containing the application data and the associated data mapping index to a file server for shared file storage. The data management system and method further accelerates shared file checkpointing in which a shared file, together with a map file that specifies how the shared file is to be distributed, is asynchronously transferred to local burst buffer memories at the nodes to accelerate reading of the shared file.

Gooding, Thomas↗

Multiparameter linear least-squares fitting to Poisson data one count at a time

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multicomponent linear model, with underlying physical count rates or fluxes which are to be estimated from the data. Despite its conceptual simplicity, the linear least-squares (LLSQ) method for solving this problem has generally been limited to situations in which the number n(sub i) of counts in each bin i is not too small, conventionally more than 5-30. It seems to be widely believed that the failure of the LLSQ method for small counts is due to the failure of the Poisson distribution to be even approximately normal for small numbers. The cause is more accurately the strong anticorrelation between the data and the wieghts w(sub i) in the weighted LLSQ method when square root of n(sub i) instead of square root of bar-n(sub i) is used to approximate the uncertainties, sigma(sub i), in the data, where bar-n(sub i) = E(n(sub i)), the expected value of N(sub i). We show in an appendix that, avoiding this approximation, the correct equations for the Poisson LLSQ (PLLSQ) problems are actually identical to those for the maximum likelihood estimate using the exact Poisson distribution. We apply the method to solve a problem in high-resolution gamma-ray spectroscopy for the JPL High-Resolution Gamma-Ray Spectrometer flown on HEAO 3. Systematic error in subtracting the strong, highly variable background encountered in the low-energy gamma-ray region can be significantly reduced by closely pairing source and background data in short segments. Significant results can be built up by weighted averaging of the net fluxes obtained from the subtraction of many individual source/background pairs. Extension of the approach to complex situations, with multiple cosmic sources and realistic background parameterizations, requires a means of efficiently fitting to data from single scans in the narrow (approximately = 1.2 keV, HEAO 3) energy channels of a Ge spectrometer, where the expected number of counts obtained per scan may be very low. Such an analysis system is discussed and compared to the method previously used.

Wheaton, Wm. A.↗