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Enabling phase quantification of anhydrous cements via Raman imaging
The phase composition of Portland cements is typically determined using conventional techniques like X-ray Diffraction (XRD) Rietveld analysis, optical microscopy point counting, and electron microscopy. However, these techniques have several limitations that may affect their accuracy in certain sample-specific scenarios. Here, we report a highly accurate phase quantification of 11 different types of commercial, anhydrous cements using a new and complementary technique: Raman imaging. Specifically, for the 4 principal phases, composition from our extensive data (250,000 Raman spectra per sample, error < 0.71%) and those obtained from XRD Rietveld and supplier data have high coefficients of determination (R{sup 2} > 0.98, mean deviation <2%). Additionally, we also quantify 8 secondary phases present in cement clinkers (gypsum, anhydrite, bassanite, syngenite, dolomite, calcite, quartz, and portlandite) with a high degree of confidence, thereby demonstrating that Raman imaging is a highly versatile tool for anhydrous phase quantification in a broad variety of cements.
Surface hopping molecular dynamics simulation of ultrafast methyl iodide photodissociation mapped by Coulomb explosion imaging
Our simulation demonstrates the robustness of the Coulomb explosion imaging technique in studying methyl iodide photodissociation, and shows that it can be effectively used for imaging non-adiabatic transitions in coordinate space.
Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging
In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.
Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images
Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.
Synthetic Aperture Radar Image Geolocation Using Fiducial Images
Synthetic Aperture Radar (SAR) creates imagery of the earth’s surface from airborne or spaceborne radar platforms. However, the nature of any radar is to geolocate its echo data, i.e., SAR images, relative to its own measured radar location. Acceptable accuracy and precision of such geolocation can be quite difficult to achieve, and is limited by any number of parameters. However, databases of geolocated earth imagery do exist, often using other imaging modalities, with Google Earth being one such example. These can often be much more accurate than what might be achievable by the radar itself. Consequently, SAR images may be aligned to some higher accuracy database, thereby improving the geolocation of features in the SAR image. Examples offer anecdotal evidence of the viability of such an approach.
Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography
We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus ‘super-resolution’). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.
Gemini Planet Imager Spectroscopy of the Dusty Substellar Companion HD 206893 B
We present new near-infrared Gemini Planet Imager (GPI) spectroscopy of HD 206893 B, a substellar companion orbiting within the debris disk of its F5V star. The J, H, K1, and K2 spectra from GPI demonstrate the extraordinarily red colors of the object, confirming it as the reddest substellar object observed to date. The significant flux increase throughout the infrared presents a challenging atmosphere to model with existing grids. Best-fit values vary from 1200 to 1800 K for effective temperature and from 3.0 to 5.0 for log(g), depending on which individual wavelength band is fit and which model suite is applied. The extreme redness of the companion can be partially reconciled by invoking a high-altitude layer of submicron dust particles, similar to dereddening approaches applied to the peculiar red field L dwarf population. However, reconciling the HD 206893 B spectra with even those of the reddest low-gravity L dwarf spectra still requires the contribution of additional atmospheric dust, potentially due to the debris disk environment in which the companion resides. Orbit fitting from 4 yr of astrometric monitoring is consistent with a ∼30 yr period, an orbital inclination of 147°, and a semimajor axis of 10 au, well within the estimated disk inner radius of ∼50 au. As one of a very few substellar companions imaged interior to a circumstellar disk, the properties of this system offer important dynamical constraints on companion–disk interaction and provide a benchmark for substellar and planetary atmospheric study.
Change-point Detection and Image Segmentation for Time Series of Astrophysical Images
Many astrophysical phenomena are time-varying, in the sense that their intensity, energy spectrum, and/or the spatial distribution of the emission suddenly change. This paper develops a method for modeling a time series of images. Under the assumption that the arrival times of the photons follow a Poisson process, the data are binned into 4D grids of voxels (time, energy band, and x-y coordinates), and viewed as a time series of non-homogeneous Poisson images. The method assumes that at each time point, the corresponding multiband image stack is an unknown 3D piecewise constant function including Poisson noise. It also assumes that all image stacks between any two adjacent change points (in time domain) share the same unknown piecewise constant function. The proposed method is designed to estimate the number and the locations of all of the change points (in time domain), as well as all of the unknown piecewise constant functions between any pairs of the change points. The method applies the minimum description length principle to perform this task. A practical algorithm is also developed to solve the corresponding complicated optimization problem. Simulation experiments and applications to real data sets show that the proposed method enjoys very promising empirical properties. Applications to two real data sets, the XMM observation of a flaring star and an emerging solar coronal loop, illustrate the usage of the proposed method and the scientific insight gained from it.
Hadamard transform imager and imaging spectrometer
An imager and a spectrometric imager, which achieve multiplexing by the use of binary optical encoding masks, have been built and tested. The masks are based on orthogonal, pseudorandom digital codes derived from Hadamard matrices. The spatial (and/or spectral) data are therefore obtained in the form of a Hadamard transform of the spatial (and/or spectral) scene. Computer algorithms are used to decode the data and reconstruct images of the original scene. The hardware, algorithms processing and display facility are described. A number of spatial and spatial/spectral images, obtained in the laboratory, are presented.
Recent developments at JPL in the application of digital image processing techniques to astronomical images
Several techniques of a digital image-processing nature are illustrated which have proved useful in visual analysis of astronomical pictorial data. Processed digital scans of photographic plates of Stephans Quintet and NGC 4151 are used as examples to show how faint nebulosity is enhanced by high-pass filtering, how foreground stars are suppressed by linear interpolation, and how relative color differences between two images recorded on plates with different spectral sensitivities can be revealed by generating ratio images. Analyses are outlined which are intended to compensate partially for the blurring effects of the atmosphere on images of Stephans Quintet and to obtain more detailed information about Saturn's ring structure from low- and high-resolution scans of the planet and its ring system. The employment of a correlation picture to determine the tilt angle of an average spectral line in a low-quality spectrum is demonstrated for a section of the spectrum of Uranus.
A comparison of digitally simulated holographic images with actual holographic images
In making digitally generated holograms the digital computer and its associated plotting equipment degrade the quality of the hologram. It is necessary then to study the computer generated hologram in terms of these degradations. A computer simulated optical processor allows the results to be observed via computer simulated images. This research reports a method for optimizing the computer image reconstruction and compares these images with optically produced holographic images. The versatility of the simulated optical processor is illustrated by several spatial filtering and hologram reconstructions.
IMAGES: An interactive image processing system
The IMAGES interactive image processing system was created specifically for undergraduate remote sensing education in geography. The system is interactive, relatively inexpensive to operate, almost hardware independent, and responsive to numerous users at one time in a time-sharing mode. Most important, it provides a medium whereby theoretical remote sensing principles discussed in lecture may be reinforced in laboratory as students perform computer-assisted image processing. In addition to its use in academic and short course environments, the system has also been used extensively to conduct basic image processing research. The flow of information through the system is discussed including an overview of the programs.
A study of image quality for radar image processing
Methods developed for image quality metrics are reviewed with focus on basic interpretation or recognition elements including: tone or color; shape; pattern; size; shadow; texture; site; association or context; and resolution. Seven metrics are believed to show promise as a way of characterizing the quality of an image: (1) the dynamic range of intensities in the displayed image; (2) the system signal-to-noise ratio; (3) the system spatial bandwidth or bandpass; (4) the system resolution or acutance; (5) the normalized-mean-square-error as a measure of geometric fidelity; (6) the perceptual mean square error; and (7) the radar threshold quality factor. Selective levels of degradation are being applied to simulated synthetic radar images to test the validity of these metrics.
Differential imaging using charge-coupled device (CCD) imagers with on-chip charge storage
A CCD technique is described which allows long integration periods between readouts together with moderate chopping rates. During each phase of a differential chopping cycle, one set of charge images is shifted to the center of the chip, which is light sensitive. At the same time, the other set of charge images is shifted under one of two masked areas which serve as charge-storage sites. Since the vertical shifting between phases can be accomplished in much less time than the typical exposure per phase, there is negligible image smearing. For a given light level and chopping rate the performance of this technique is limited primarily by the readout noise and the vertical charge transfer inefficiency. If many transfer cycles are required between readouts, the charge transfer inefficiency will diffuse the accumulated charge images along the vertical columns. Laboratory and astronomical data obtained with this technique using a CCD camera and the 2.3 m telescope on Kitt Peak are presented.
Imaging spectrometer - An advanced multispectral imaging concept
The concept of an imaging spectrometer, which is being studied as a potential Space Shuttle experiment, is evaluated as a 'push-broom' imager that includes a spectrometer to disperse each line of imaging information into its spectral components. Using this instrument, the dispersed energy falls upon a two-dimensional focal plane array that detects both spatial and spectral information. As the line field of view is advanced over the earth by the motion of the spacecraft, the focal plane is read out constantly, which produces 'push-broom' images at multiple wavelengths. Ground instantaneous fields of view of 10 m in the visual and 20 m in the infrared are provided by the system, at a spectral resolution of 20 nm over the range from 0.4-2.5 microns. The system utilizes a triple-pass Schmidt optical system with a mosaic focal plane. A subset of the data stream is selected and encoded for transmission by the use of onboard processing.
Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images
Airborne Imaging Spectrometer-1 (AIS-1) data were obtained for an area of amphibolite grade metamorphic rocks that have moderate rangeland vegetation cover. Although rock exposures are sparse and patchy at this site, soils are visible through the vegetation and typically comprise 20 to 30 percent of the surface area. Channel averaged low band depth images for diagnostic soil rock absorption bands. Sets of three such images were combined to produce color composite band depth images. This relative simple approach did not require extensive calibration efforts and was effective for discerning a number of spectrally distinctive rocks and soils, including soils having high talc concentrations. The results show that the high spectral and spatial resolution of AIS-1 and future sensors hold considerable promise for mapping mineral variations in soil, even in moderately vegetated areas.