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NASA NTRS1994

Unsupervised Segmentation Of Polarimetric SAR Data

Method of unsupervised segmentation of polarimetric synthetic-aperture-radar (SAR) image data into classes involves selection of classes on basis of multidimensional fuzzy clustering of logarithms of parameters of polarimetric covariance matrix. Data in each class represent parts of image wherein polarimetric SAR backscattering characteristics of terrain regarded as homogeneous. Desirable to have each class represent type of terrain, sea ice, or ocean surface distinguishable from other types via backscattering characteristics. Unsupervised classification does not require training areas, is nearly automated computerized process, and provides nonsubjective selection of image classes naturally well separated by radar.

Rignot, Eric J.↗

NASA NTRS2004

Time Variable Gravity from Local Mascon Analysis of GRACE Data

We have analyzed GRACE Level 1-B data in 2003 and assessed a new approach for extracting time variable gravity that isolates the gravity signal in both time and space. The Level-1B satellite-to-satellite range rate (KBRR) data and accelerometry are processed in daily arcs using the precise orbit products produced by the GRACE team from GPS to calibrate both the accelerometer and KBRR data. We then adjusted select components of the intersatellite baseline vector for each data segment isolated to the region of interest. Herein, we solved for mass anomalies in 45 deg x 45 deg blocks over the Amazon and the nearby Atlantic Ocean and estimate mass flux in units of cm of water over each block. We show with this approach that we can recover mass anomalies on a submonthly basis with 10 to 15 day temporal resolution. We discuss the important issues related to this solution, including the size of the mascon blocks, the weight given to the temporal and spatial constraint used to stabalize the solutions, as well as the optimal correlation in time and distance. We compare the the mascon results with solutions obtained from the more standard approach using spherical harmonics and with independent hydrology models and lake data. This technique demonstrates that sub-monthly medium wavelength mass flux phenomena are well sensed by the hyper-precise line of sight velocity data produced from GRACE.

Lemoine, Frank G.↗

NASA NTRS1976

Investigation of spatial misregistration effects in multispectral scanner data

The author has identified the following significant results. A model for estimating the expected proportion of multiclass pixels in a scene was generalized and extended to include misregistration effects. Another substantial effort was the development of a simulation model to generate signatures to represent the distributions of signals from misregistered multiclass pixels, based on single class signatures. Spatial misregistration causes an increase in the proportion of multiclass pixels in a scene and a decorrelation between signals in misregistered data channels. The multiclass pixel proportion estimation model indicated that this proportion is strongly dependent on the pixel perimeter and on the ratio of the total perimeter of the fields in the scene to the area of the scene. Test results indicated that expected values computed with this model were similar to empirical measurements made of this proportion in four LACIE data segments.

Nalepka, R. F.↗

NASA NTRS1975

S-193 scatterometer backscattering cross section precision/accuracy for Skylab 2 and 3 missions

Procedures for measuring the precision and accuracy with which the S-193 scatterometer measured the background cross section of ground scenes are described. Homogeneous ground sites were selected, and data from Skylab missions were analyzed. The precision was expressed as the standard deviation of the scatterometer-acquired backscattering cross section. In special cases, inference of the precision of measurement was made by considering the total range from the maximum to minimum of the backscatter measurements within a data segment, rather than the standard deviation. For Skylab 2 and 3 missions a precision better than 1.5 dB is indicated. This procedure indicates an accuracy of better than 3 dB for the Skylab 2 and 3 missions. The estimates of precision and accuracy given in this report are for backscattering cross sections from -28 to 18 dB. Outside this range the precision and accuracy decrease significantly.

Krishen, K.↗

NASA NTRS1979

Simpler cabling and power link for remote readouts

Display power and segment data are multiplexed over same coaxial line. Thus, only one wire and return lead are needed, and single power supply at central location can service all remote displays.

Perry, J. C.↗

NASA NTRS1982

Can crop types be resolved using mixture distribution components - Some initial results and implications

For the analysis of remotely sensed data, it is frequently necessary to design a classifier in order to locate a ground cover class of interest or to estimate the proportion of this ground cover class. Advantages of a mixture distribution formulation are discussed, and a description is presented of the results of estimating the proportion of small grains in ten Landsat data segments using the mixture model. It is found that the mixture model proportion estimates have a very low variance and coefficient of variation. The discussed investigation implies that the mixtures model is a viable method for determining the distributions of classes of interest in remote sensing problems and in estimating the proportions of these classes directly.

Lennington, R. K.↗

NASA NTRS1988

A primitive-based 3D object recognition system

An intermediate-level knowledge-based system for decomposing segmented data into three-dimensional primitives was developed to create an approximate three-dimensional description of the real world scene from a single two-dimensional perspective view. A knowledge-based approach was also developed for high-level primitive-based matching of three-dimensional objects. Both the intermediate-level decomposition and the high-level interpretation are based on the structural and relational matching; moreover, they are implemented in a frame-based environment.

Dhawan, Atam P.↗

NASA NTRS1992

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.

Kruse, F. A.↗

NASA NTRS2005

Discovery of Fast X-ray Oscillations During the 1998 Giant Flare from SGR 1900+14

We report the discovery of complex high frequency variability during the August 27, 1998 giant flare from SGR 1900+14 using the Rossi X-ray Timing Explorer (RXTE). We detect an approx. equals 84 Hz oscillation (QPO) during a 1 s interval beginning approximately 1 min after the initial hard spike. The amplitude is energy dependent, reaching a maximum of 26% (rms) for photons above 30 keV, and is not detected below 11 keV, with a 90% confidence upper limit of 14% (rms). Remarkably, additional QPOs are detected in the average power spectrum of data segments centered on the rotational phase at which the 84 Hz signal was detected. Two signals, at 53.5 and 155.1 Hz, are strongly detected, while a third feature at 28 Hz is found with lower significance. These QPOs are not detected at other rotational phases. The phenomenology seen in the SGR 1900+14 flare is similar to that of QPOs recently reported by Israel et al. (2005) from the December 27, 2004 flare from SGR 1806-20, suggesting they may have a common origin, perhaps torsional vibrations of the neutron star crust. Indeed, an association of the four frequencies (in increasing order) found in SGR 1900+14 with l = 2, 4, 7, and 13 toroidal modes appears plausible. We discuss our findings in the context of this model and show that if the stars have similar masses then the magnetic field in SGR 1806-20 must be about twice as large as in SGR 1900+14, broadly consistent with estimates from pulse timing.

Strohmayer, T.↗

NASA NTRS1999

Loss tolerant speech decoder for telecommunications

A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer. If a speech frame is detected to be lost or in error, then extrapolation modules are executed and replacement SCA parameters are generated and sent as the parameters required by the SCA. In this way, the information transfer to the SCA is transparent, and the SCA processing continues as usual. The listener will not normally notice that a speech frame has been lost because of the smooth transition between the last-received, lost, and next-received speech frames.

Prieto, Jr., Jaime L.↗

NASA NTRS2017

In-Situ Observations of Longitudinal Compression Damage in Carbon-Epoxy Cross Ply Laminates Using Fast Synchrotron Radiation Computed Tomography

The role of longitudinal compressive failure mechanisms in notched cross-ply laminates is studied experimentally with in-situ synchrotron radiation based computed tomography. Carbon/epoxy specimens loaded monotonically in uniaxial compression exhibited a quasi-stable failure process, which was captured with computed tomography scans recorded continuously with a temporal resolutions of 2.4 seconds and a spatial resolution of 1.1 microns per voxel. A detailed chronology of the initiation and propagation of longitudinal matrix splitting cracks, in-plane and out-of-plane kink bands, shear-driven fiber failure, delamination, and transverse matrix cracks is provided with a focus on kink bands as the dominant failure mechanism. An automatic segmentation procedure is developed to identify the boundary surfaces of a kink band. The segmentation procedure enables 3-dimensional visualization of the kink band and conveys the orientation, inclination, and spatial variation of the kink band. The kink band inclination and length are examined using the segmented data revealing tunneling and spatial variations not apparent from studying the 2-dimensional section data.

Bergan, Andrew C.↗

NASA NTRS2024

PACE OCI Calibration and Geolocation Operational Algorithm Description

This technical report describes the software implementation of the calibration and geolocation processing algorithms for the Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. PACE was launched on February 8, 2024. The first Earth-viewing data were collected on February 25, and commissioning was completed on April 5. All PACE science data are acquired and processed by the Science Data Segment (SDS). The first processing stages are Level 0-to-1A and Level 1A-to-1B. The calibration and geolocation processing is performed during the latter stage. The L1B products are the inputs for geophysical retrieval processing (Level 2). This report is organized as follows. The pertinent characteristics of OCI for calibration and geolocation processing are described in Section II. Section III describes the implementation of the geolocation processing algorithms, and the calibration processing is described in Section IV. The Level 1B product format is described in Section V.

Ivona Cetinic↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

PACE↗

NASA NTRS1973

Optical data processing using paraboloidal mirror segments

An optical data processing system using paraboloidal reflecting surfaces is disclosed. In the preferred embodiment the paraboloidal reflecting surfaces are segments of a paraboloidal mirror. A source of coherent light is in the focal plane of the first paraboloidal mirror segment which collimates the beam and reflects it toward a second paraboloidal mirror surface. The information to be analyzed, on a transparency for example, is placed in the collimated beam. The beam is reflected from the second paraboloidal mirror segment and focused on a Fourier transform plane. A photon detector could be placed in the Fourier transform plane or suitable spatial filters, with the filtered beam then being reflected from a third paraboloidal mirror segment to be focused on a reconstruction plane.

Husain-Abidi, A. S.↗

NASA NTRS1981

Recommended data sets, corn segments and spring wheat segments, for use in program development

The sets of Large Area Crop Inventory Experiment sites, crop year 1978, which are recommended for use in the development and evaluation of classification techniques based on LANDSAT spectral data are presented. For each site, the following exists: (1) accuracy assessment digitized ground truth; (2) a minimum of 5 percent of the scene ground truth identified as corn or spring wheat; and (3) at least four acquisitions of acceptable data quality during the growing season of the crop of interest. The recommended data sets consist of 41 corn/soybean sites and 17 spring wheat sites.

Austin, W. W.↗

NASA NTRSDate not supplied

Developing Mission Segment-Specific Clinical Data for Impact

Space exploration missions represent a significant future objective for NASA’s human spaceflight operations for the near and long term. By operating missions during longer periods and over further distances, NASA’s mission planning models, hardware systems, and medical capabilities will be stretched beyond their original design constraints. The necessity of medical training and medical supply manifesting for such missions is likely to become incredibly intricate as trade-space analysis is performed over varying mission profiles. For this purpose, IMPACT was created to extend and improve the Integrated Medical Model (IMM) which is used for International Space Station (ISS) medical predications and consists of a probabilistic risk assessment tool that simulates missions to calculate risks given the constraints on crew complement, mission duration and activity (such as Extravehicular Activities), power, communication, mass, volume, and evacuation consideration as well as to predict mission outcomes. Currently, the medical evidence used to support IMPACT remains static, addressing risks of a medical condition and the impacts of the condition during spaceflight only, but is only calculated for a small number of mission parameters. Extended missions will consist of new and varied living environments, changing gravitational loads, and dynamic working requirements. Medical risks are not typically constant but will change depending upon the physical and physiological environment of the body during all phases of a mission. Risks are likely to shift during different mission segments, and understanding how components of a mission profile change risks will assist mission planners to increase chances for mission success.

Christopher Zahner↗

NASA NTRS1992

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique. The results are discussed and compared with those obtained using supervised techniques.

Rignot, Eric J. M.↗