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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Image understanding and interpretation in microwave diversity imaging

The authors investigate microwave imaging of metallic objects using a diversity method and interpret and predict the reconstructed image from an approach based on analysis of the scattering mechanism and a back-projection algorithm used in image retrieval. The connection between the various scattering mechanisms and the reconstructed images is discussed, what the images represent is interpreted, and a prediction is made as to what the image will look like over given spectral and angular windows. A brief description is given of the microwave diversity imaging system and the formulation of the microwave diversity imaging based on the physical optics approximation. The scattering mechanism of a complex shaped metallic object is then briefly reviewed and an alternate approach to interpreting the reconstructed image based on the understanding of the scattering mechanism and the reconstruction algorithm is given. Several numerical and experimental examples are included to support this interpretation approach.

Li, Hsueh-Jyh↗
NASA NTRS1996

Parallelization and Algorithmic Enhancements of High Resolution IRAS Image Construction

The Infrared Astronomical Satellite caried out a nearly complete survey of the infrared sky, and the survey data are important for the study of many astrophysical phenomena. However, many data sets at other wavelengths have higher resolutions than that of the co-added IRAS maps, and high resolution IRAS images are strongly desired both for their own information content and their usefulness in correlation. The HIRES program was developed by the Infrared Processing and Analysis Center (IPAC) to produce high resolution (approx. 1') images from IRAS data using the Maximum Correlation Method (MCM). We describe the port of HIRES to the Intel Paragon, a massively parallel supercomputer, other software developments for mass production of HIRES images, and the IRAS Galaxy Atlas, a project to map the Galactic plane at 60 and 100(micro)m.

Satellite infrared IRAS IRAS maps IRAS images↗
NASA NTRS1993

Determining the 3-D structure and motion of objects using a scanning laser range sensor

In order for the EVAHR robot to autonomously track and grasp objects, its vision system must be able to determine the 3-D structure and motion of an object from a sequence of sensory images. This task is accomplished by the use of a laser radar range sensor which provides dense range maps of the scene. Unfortunately, the currently available laser radar range cameras use a sequential scanning approach which complicates image analysis. Although many algorithms have been developed for recognizing objects from range images, none are suited for use with single beam, scanning, time-of-flight sensors because all previous algorithms assume instantaneous acquisition of the entire image. This assumption is invalid since the EVAHR robot is equipped with a sequential scanning laser range sensor. If an object is moving while being imaged by the device, the apparent structure of the object can be significantly distorted due to the significant non-zero delay time between sampling each image pixel. If an estimate of the motion of the object can be determined, this distortion can be eliminated; but, this leads to the motion-structure paradox - most existing algorithms for 3-D motion estimation use the structure of objects to parameterize their motions. The goal of this research is to design a rigid-body motion recovery technique which overcomes this limitation. The method being developed is an iterative, linear, feature-based approach which uses the non-zero image acquisition time constraint to accurately recover the motion parameters from the distorted structure of the 3-D range maps. Once the motion parameters are determined, the structural distortion in the range images is corrected.

Nandhakumar, N.↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.

deep learning↗
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1973

Terrain type recognition using ERTS-1 MSS images

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Gramenopoulos, N.↗
NASA NTRS1994

Perceptual Optimization of DCT Color Quantization Matrices

Many image compression schemes employ a block Discrete Cosine Transform (DCT) and uniform quantization. Acceptable rate/distortion performance depends upon proper design of the quantization matrix. In previous work, we showed how to use a model of the visibility of DCT basis functions to design quantization matrices for arbitrary display resolutions and color spaces. Subsequently, we showed how to optimize greyscale quantization matrices for individual images, for optimal rate/perceptual distortion performance. Here we describe extensions of this optimization algorithm to color images.

Watson, Andrew B.↗
NASA NTRS2004

Bayesian-Augmented Identification of Stars in a Narrow View

An algorithm for the identification of stars from a charge-coupled-device (CCD) image of a star field has been extended for use with narrower field-of-view images. Previously, the algorithm had been shown to be effective at a field of view of 8 degrees. This work augments the earlier algorithm using Bayesian decision theory. The new algorithm is shown to be capable of effective star identification down to a field of view of 2 degrees. The algorithm was developed for use in estimating the attitude of a spacecraft and could be used on Earth to help in the identification of stars and other celestial objects for astronomical observations.

Clouse, Daniel↗
NASA NTRS2006

Tracking the Martian CO2 Polar Ice Caps in Infrared Images

Researchers at NASA s Jet Propulsion Laboratory have developed a method for automatically tracking the polar caps on Mars as they advance and recede each year (see figure). The seasonal Mars polar caps are composed mainly of CO2 ice and are therefore cold enough to stand out clearly in infrared data collected by the Thermal Emission Imaging System (THEMIS) onboard the Mars Odyssey spacecraft. The Bimodal Image Temperature (BIT) histogram analysis algorithm analyzes raw, uncalibrated data to identify images that contain both "cold" ("polar cap") and "warm" ("not polar cap") pixels. The algorithm dynamically identifies the temperature that separates these two regions. This flexibility is critical, because in the absence of any calibration, the threshold temperature can vary significantly from image to image. Using the identified threshold, the algorithm classifies each pixel in the image as "polar cap" or "not polar cap," then identifies the image row that contains the spatial transition from "polar cap" to "not polar cap." While this method is useful for analyzing data that has already been returned by THEMIS, it has even more significance with respect to data that has not yet been collected. Instead of seeking the polar cap only in specific, targeted images, the simplicity and efficiency of this method makes it feasible for direct, onboard use. That is, THEMIS could continuously monitor its observations for any detections of the polar-cap edge, producing detections over a wide range of spatial and temporal conditions. This effort can greatly contribute to our understanding of long-term climatic change on Mars.

Wagstaff, Kiri L.↗
NASA NTRS1992

Registration of interferometric SAR images

Interferometric synthetic aperture radar (INSAR) is a new way of performing topography mapping. Among the factors critical to mapping accuracy is the registration of the complex SAR images from repeated orbits. A new algorithm for registering interferometric SAR images is presented. A new figure of merit, the average fluctuation function of the phase difference image, is proposed to evaluate the fringe pattern quality. The process of adjusting the registration parameters according to the fringe pattern quality is optimized through a downhill simplex minimization algorithm. The results of applying the proposed algorithm to register two pairs of Seasat SAR images with a short baseline (75 m) and a long baseline (500 m) are shown. It is found that the average fluctuation function is a very stable measure of fringe pattern quality allowing very accurate registration.

Lin, Qian↗
NASA NTRS1990

Locating A Planar Target From An Image

Report describes experiments to test two algorithms being considered for use in automatic control of robots and remote manipulators. One algorithm based on quadrangle-projection method, which involves use of four target points lying at corners of quadrangle. Other algorithm based on elastic-matching ("rubber-mask") approach, in which reference image warped to conform to actual image. Both algorithms determine position and orientation of target with accuracy sufficient for consistent and efficient acquisition by telerobot.

Cornils, Karin↗
NASA NTRS1986

Image Interpolation With Dedicated Digital Hardware

Algorithm for interpolating two-dimensional image data to change picture-element spacing implemented in dedicated digital hardware for high-speed execution. System interpolates 100 times as fast as generalpurpose computer. Image resampling occurs first along one image axis and then along other, using two interpolation devices implemented in series.

Hartenstein, R.↗
NASA NTRS1988

Automatic analysis of stereoscopic image pairs from GOES satellites

An algorithm for automatic analysis of stereoscopic images is applied to stereo pairs of severe local storms and hurricanes observed by two GOES satellites. The automatically derived height fields and manual analyses are compared. It is found that the automatic analysis produces a more detailed structure in less time than manual analysis, although the two methods have similar quality. In areas where the features are very small, however, it is suggested that manual analysis is superior.

Hasler, A. F.↗
NASA NTRS2020

Tropopause and lower stratosphere winds and eddy fluxes on Saturn as seen by Cassini imaging

An automated cloud feature tracking algorithm is applied to 2004 Cassini Imaging Science Subsystem images of Saturn's southern hemisphere and equatorial region (5°N-70°S) in continuum, methane band, and ultraviolet filters to derive zonal wind profiles and eddy momentum fluxes from the middle troposphere cloud tops to the lower stratosphere. Zonal winds in the eastward and westward jets decrease in strength from the cloud tops (≳ 1 bar) to the upper troposphere (~60–600 mb), but do not measurably change in strength from the upper troposphere to the tropopause (~60 mb) to the lower stratosphere (≲ 60 mb). A narrow (~3°N-3°S) equatorial jet is detected at the cloud tops, but a previously reported strengthening of this jet from the cloud top level to the upper troposphere/tropopause region cannot be verified due to an absence of easily trackable features at the equator and conflicting results for the few features available. Eddy momentum fluxes on either side of the jet cores outside the tropics (poleward of ~20°S) converge into the eastward jets and diverge from the westward jets. Whether the flux convergence into the eastward jets weakens from the visible cloud level to the upper troposphere cannot be determined for this limited time period, but the eddy convergence appears to increase from the troposphere to the lower stratosphere. The divergence of the eddy flux from the westward jets seems to more clearly increase from the middle to the upper troposphere to the stratosphere, but this does not explain the slight weakening of the westward jets with height.

Saturn↗
NASA NTRS1995

Calibration to surface reflectance of terrestrial imaging spectrometry data: Comparison of methods

Many algorithms for spectral analysis of imaging spectroscopy data of the Earth's surface require that the data be calibrated to surface reflectance. Calibration requires removing instrumental response, solar irradiance, atmospheric transmittance, and atmospheric scattering from the radiance detected at the sensor. Depending on the amount of support data, this can be a formidable task. This paper examines four methods of calibration: (1) a radiative transfer model from the University of Colorado (ATREM: Gao and Goetz, 1990; Gao et al., 1992), (2) a MODTRAN-based method developed at the Jet Propulsion Lab by Green et al., (1191), (3) a ground calibration using known sites as standards, and (4) a combined approach using radiative transfer methods and ground calibration. Data from the Airborne Visual and Infra-Red Imaging Spectrometer (AVIRIS) instrument were evaluated from data sets obtained over multiple years and multiple sites.

Clark, Roger N.↗