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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 217 records · Page 12

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Lastly, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

42 ENGINEERING↗

Improving Cloud Detection in Satellite Images of Coral Reef Environments Using Space Shuttle Photographs and High-Definition Television

Coral reefs worldwide are suffering from severe and rapid degradation (Bryant et A, 1998; Hoegh-Guldberg, 1999). Quick, consistent, large-scale assessment is required to assess and monitor their status (e.g., USDOC/NOAA NESDIS et al., 1999). On-going systematic collection of high resolution digital satellite data will exhaustively complement the relatively small number of SPOT, Landsat 4-5, and IRS scenes acquired for coral reefs the last 20 years. The workhorse for current image acquisition is the Landsat 7 ETM+ Long Term Acquisition Plan (Gasch et al. 2000). Coral reefs are encountered in tropical areas and cloud contamination in satellite images is frequently a problem (Benner and Curry 1998), despite new automated techniques of cloud cover avoidance (Gasch and Campana 2000). Fusion of multidate acquisitions is a classical solution to solve the cloud problems. Though elegant, this solution is costly since multiple images must be purchased for one location; the cost may be prohibitive for institutions in developing countries. There are other difficulties associated with fusing multidate images as well. For example, water quality or surface state can significantly change through time in coral reef areas making the bathymetric processing of a mosaiced image strenuous. Therefore, another strategy must be selected to detect clouds and improve coral reefs mapping. Other supplemental data could be helpful and cost-effective for distinguishing clouds and generating the best possible reef maps in the shortest amount of time. Photographs taken from the 1960s to the present from the Space Shuttle and other human-occupied spacecraft are one under-used source of alternative multitemporal data (Lulla et al. 1996). Nearly 400,000 photographs have been acquired during this period, an estimated 28,000 of these taken to date are of potential value for reef remote sensing (Robinson et al. 2000a). The photographic images can be digitized into three bands (red, green and blue) and processed for various applications (e.g., Benner and Curry 1998, Nedeltchev 1999, Glasser and Lulla 2000, Robinson et al. 2000c, Webb et al, in press).

Andrefeouet, Serge↗

Capability of Thermographic Imaging Defined for Detection in High-Temperature Composite Materials

Significant effort and resources are being expended to develop ceramic matrix (CMC), metal matrix (MMC), and polymer matrix (PMC) composites for high-temperature engine components and other parts in advanced aircraft. The objective of this NASA Lewis Research Center study was to evaluate the ability of a thermographic imaging technique for detecting artificially created defects (flat-bottom holes) of various diameters and depths in four composite systems (two CMC's, one MMC, and one PMC) of interest as high-temperature structural materials.

Roth, Don J.↗

Feature detection in satellite images using neural network technology

A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

Augusteijn, Marijke F.↗

Mercury - Full-disk radar images and the detection and stability of ice at the North Pole

The first full-disk radar images of Mercury are discussed. About 77 percent of the surface was imaged at resolutions as good as 150 km. The North Pole was visible at the time of the observations, and the feature with the highest same sense (SS) circular reflectivity in the images is near the nominal polar position. The peak SS reflectivity of this feature is 7.9 percent and the circular polarization ratio through much of it is over one. The diameter of the feature is estimated to be less than about 350 km. The signal strength and polarization characteristics suggest the presence of very clean ices deposited in a relatively short period. The reflectivity of the north polar feature is slightly depressed compared to absolute reflectivities of other ice bodies and regions in the solar system. This may be due to a layer of dust or soil covering the ice. Other prominent features in the unphotographed hemisphere correpond to positions where atmospheric sodium enhancements have been measured from earth. These sites may be large basins similar to the Caloris basin.

Butler, Bryan J.↗

Algorithm for Detecting a Bright Spot in an Image

An algorithm processes the pixel intensities of a digitized image to detect and locate a circular bright spot, the approximate size of which is known in advance. The algorithm is used to find images of the Sun in cameras aboard the Mars Exploration Rovers. (The images are used in estimating orientations of the Rovers relative to the direction to the Sun.) The algorithm can also be adapted to tracking of circular shaped bright targets in other diverse applications. The first step in the algorithm is to calculate a dark-current ramp a correction necessitated by the scheme that governs the readout of pixel charges in the charge-coupled-device camera in the original Mars Exploration Rover application. In this scheme, the fraction of each frame period during which dark current is accumulated in a given pixel (and, hence, the dark-current contribution to the pixel image-intensity reading) is proportional to the pixel row number. For the purpose of the algorithm, the dark-current contribution to the intensity reading from each pixel is assumed to equal the average of intensity readings from all pixels in the same row, and the factor of proportionality is estimated on the basis of this assumption. Then the product of the row number and the factor of proportionality is subtracted from the reading from each pixel to obtain a dark-current-corrected intensity reading. The next step in the algorithm is to determine the best location, within the overall image, for a window of N N pixels (where N is an odd number) large enough to contain the bright spot of interest plus a small margin. (In the original application, the overall image contains 1,024 by 1,024 pixels, the image of the Sun is about 22 pixels in diameter, and N is chosen to be 29.)

Source record↗

AOTF Polarimetric Hyperspectral Imaging for Mine Detection.

This paper reports an analysis of polarimetric hyperspectral imagery of an outdoor scene containing a number of inactive mines. The imagery was taken with a visible/near-infrared acousto-optic tunable filter prototype system. The result has illustrated that the characteristics of mine smooth surfaces can create high signals in polarization difference images and consequently provide a quick and reliable approach for detection.

['Mine↗

Optimum detection of an optical image on a photoelectric surface

The detection of an optical image in the presence of uniform background light is based on a likelihood ratio formed of the numbers of photoelectrons emitted from small elements of a photoelectric surface onto which the image is focused. When diffraction is negligible and the surface has unit quantum efficiency, this detector is equipollent with the optimum detector of the image forming light. Its performance is compared with that of the threshold detector and that of a detector basing its decisions on the total number of photoelectrons from a finite area of the image. The illuminance of the image is postulated to have a Gaussian spatial distribution. All three detectors exhibit nearly the same reliability.

Helstrom, C. W.↗

Boundary layer transition detection by luminescence imaging

In recent experiments we have demonstrated the feasibility of a new approach to boundary layer transition detection. This new approach employs the temperature dependence of certain photoluminescent materials in the form of a surface coating or 'paint' to detect the change in heat transfer characteristics that accompany boundary layer transition. The feasibility experiments were conducted for low subsonic to transonic Mach numbers on two-dimensional airfoil and flat plate configurations. Paint derived transition locations were determined and compared to those obtained from Preston pressure probe measurements. Artificial heating of the models was used to obtain transition temperature signatures suitable for the instrumentation available to us. Initial estimates show, however, that passive kinetic heating at high Mach numbers is a promising alternative.

Mclachlan, B. G.↗

SEEDS Direct Imaging of the RV-Detected Companion to V450 Andromedae, and Characterization of the System

We report the direct imaging detection of a low-mass companion to a young, moderately active star V450 And, that was previously identified with the radial velocity (RV) method. The companion was found in high-contrast images obtained with the Subaru Telescope equipped with the HiCIAO camera and AO188 adaptive optics system. From the public ELODIE and SOPHIE archives we extracted available high-resolution spectra and RV measurements, along with RVs from the Lick planet search program. We combined our multi-epoch astrometry with these archival, partially unpublished RVs, and found that the companion is a low-mass star, not a brown dwarf, as previously suggested. We found the best-fitting dynamical masses to be m(sub 1) = 1.141(sup + 0.037, sub - 0.091) and m(sub 2) = 0.279(sup + 0.023, sub - 0.020) solar mass. We also performed spectral analysis of the SOPHIE spectra with the iSpec code. Hipparcos time-series photometry shows a periodicity of P = 5.743 day, which is also seen in the SOPHIE spectra as an RV modulation of the star A. We interpret it as being caused by spots on the stellar surface, and the star to be rotating with the given period. From the rotation and level of activity, we found that the system is 380(sup + 220, sub -100) Myr old, consistent with an isochrone analysis (220 (sup +2120, sub -90) Myr). This work may serve as a test case for future studies of low mass stars, brown dwarfs, and exoplanets by combination of RV and direct imaging data.

direct imaging detection of a low-mass companion↗

UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. Here, the architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method.

42 ENGINEERING↗

Direct and interferometric imaging approaches for detecting earth-like extrasolar planets

This paper discusses functional requirements of space-based observational systems with sufficient sensitivity, resolution, and dynamic range to image earth-like extrasolar planets within a search radius of 10 parsecs from the sun. Both direct and interferometric systems operating at visible and infrared wavelengths are evaluated, and the methods used to establish the system tolerances are presented. Due to the more favorable star/planet contrast ratio in the infrared, optical tolerance requirements are less stringent than in the visible. However, reduction of thermal radiation from the telescope requires cooling of the primary optics. Other tradeoffs between various approaches are enumerated.

Diner, D. J.↗

Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗

Synthetic aperture radar target detection, feature extraction, and image formation techniques

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Li, Jian↗

Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 micron water vapor band

Using special images acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) at 20 km altitude, we show that wavelengths close to the center of the strong 1.38 micron water vapor band are useful for detecting thin cirrus clouds. The detection makes use of the fact that cirrus clouds are located above almost all the atmospheric water vapor. Because of the strong water vapor absorption in the lower atmosphere, AVIRIS channels near 1.38 micron receive little scattered solar radiance from the surface of low level clouds. When cirrus clouds are present, however, these channels receive large amounts of scattered solar radiance from the cirrus clouds. Our ability to determine cirrus cloud cover using space-based remote sensing will be improved if channels near the center of the 1.38 micron water vapor band are added to future satellites.

Gao, Bo-Cai↗