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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase.

Vane, G.↗
NASA NTRS1986

Proceedings of the Second Airborne Imaging Spectrometer Data Analysis Workshop

The Second Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on May 6, 7, and 8, 1986. It was attended by 100 people from seven countries. Papers were presented by 24 attendees; summaries of 19 of the papers are published in these Workshop Proceedings. The Second Workshop was divided into three sessions: Calibration, the Atmosphere, and Data Techniques; Geological Research; and Botanical and Geobotanical Research. Because many of the AIS researchers have had an additional year to work with their data, a general advance was seen in the utilization of the data for solving earth science problems. The major conclusions that can be drawn from the Workshop are: (1) Almost everyone now working with AIS data is attempting to take into account in data analysis the effects of the atmosphere and sensor calibration and performance. As a result, information extraction techniques are becoming more sophisticated and effective. (2) The emphasis on work in the geological disciplines is beginning to shift to the application of AIS data for solving geological problems now that the efficacy of the data for mineral identification has been demonstrated. (3) In the botanical sciences, interpretation of AIS data is becoming more quantitative. Several statistical techniques are under development for extracting the subtle differences in plant spectra in the region of the near infrared and short wavelength infrared. In summary, an overall advance was seen in the ability to effectively utilize the fundamentally new class of data acquired with imaging spectrometers. There was also a general feeling at the Workshop however, that much work remains to be done in remote sensing research before the maximum potential of these data for solving earth science problems is realized.

Gregg Vane↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2025

EcoBOT: an AI/ML enabled automated phenotyping capability for model plants

Introduction: Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods: The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results: The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion: The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.

AI image analysis↗
NASA NTRS1987

Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks

Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.

Feldman, Sandra C.↗
NASA NTRS1986

Airborne spectroradiometry: The application of AIS data to detecting subtle mineral absorption features

Analysis of Airborne Imaging Spectrometer (AIS) data acquired in Australia has revealed a number of operational problems. Horizontal striping in AIS imagery and spectral distortions due to order overlap were investigated. Horizontal striping, caused by grating position errors can be removed with little or no effect on spectral details. Order overlap remains a problem that seriously compromises identification of subtle mineral absorption features within AIS spectra. A spectrometric model of the AIS was developed to assist in identifying spurious spectral features, and will be used in efforts to restore the spectral integrity of the data.

Cocks, T. D.↗
DOE OSTI2025

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗
NASA NTRS1986

Near-infrared detection of ammonium minerals at Ivanhoe Hot Springs, Nevada

Airborne Imaging Spectrometer (AIS) data were collected over the fossil hot spring deposit at Ivanhoe, Nevada in order to determine the surface distribution of NH4-bearing minerals. Laboratory studies show that NH4-bearing minerals have characteristic absorption features in the near-infrared (NIR). Ammonium-bearing feldspars and alunites were observed at the surface of Ivanhoe using a hand-held radiometer. However, first look analysis of the AIS images showed that the line was about 500 m east of its intended mark, and the vegetation cover was sufficiently dense to inhibit preliminary attempts at making relative reflectance images for detection of ammonium minerals.

Krohn, M. D.↗
NASA NTRS1986

Analysis of AIS data of the Bonanza Creek Experimental Forest, Alaska

Airborne Imaging Spectrometer (AIS) data were acquired in 1985 over the Bonanza Creek Experimental Forest, Alaska for the analysis of canopy characteristics including biochemistry. Concurrent with AIS overflights, foliage from fifteen coniferous and deciduous forest stands were analyzed for a variety of biochemical constituents including nitrogen, lignin, protein, and chlorophyll. Preliminary analysis of AIS spectra indicates that the wavelength region between 1450 to 1800 namometers (nm) displays distinct differences in spectral response for some of the forest stands. A flat field subtraction (forest stand spectra - flat field spectra) of the AIS spectra assisted in the interpretation of features of the spectra that are related to biology.

Spanner, M. A.↗
NASA NTRS1986

Analysis of AIS radiometry at Mono Lake, California

Airborne imaging spectrometer (AIS) data were studied to establish absolute instrumental calibration and to provide atmospheric corrections. Good agreement is found between calculated and measured radiances for uniform surface targets (beaches), but simulations of atmospheric properties with LOWTRAN lead to unreasonably low values of atmospheric precipitable water. Absorptions from CO2 are not detected in the AIS data, but are strongly present in the LOWTRAN model. The apparent low contrast of all atmospheric absorption bands leads to a study of contamination from overlapping spectral orders in the AIS data. The suspected contamination is shown unambiguously to be present beyond 1500 nm. The magnitude remains uncertain. Spectral band filling at 1400 nm cannot be accounted for by order mixing because of the 800 nm blocking filter used. Rough corrections for short wavelength mode observations might be possible if an after-the-fact radiometric calibration of the instrument can be developed.

Conel, J. E.↗
NASA NTRS1985

Analysis of AIS Data of the Recluse Oil Field, Recluse, Wyoming

Airborne Imaging Spectrometer (AIS) data were flown over the Recluse, Wyoming oil field on September 9, 1984. Processing software was developed at Earth Satellite Corporation (EarthSat) for interactive analysis of the AIS data. EarthSat's AIS processing capabilities include destriping, solar irradiance corrections, residual calculations, geometric resampling, equal energy normalization, interactive spectral classifications and a variety of compressive algorithms to reduce the data to 8-bit format with a minimum of information loss. The in-house photolab facilities of EarthSat can routinely produce high-quality color renditions of the enhanced AIS data. A total of 80 lithologic samples were collected under the AIS flight lines. Correlation (within the atmospheric windows) between the laboratory and the AIS spectra of sample sites was generally poor. Reasonable correlation was only possible in large, freshly plowed fields. Mixed pixels and contrast between the natural and sample's surfaces were believed responsible for the poor correlation. Finally, a drift of approximately three channels was observed in the diffraction grating position within the 1.8 to 2.1 micron quadrant.

Dykstra, J. D.↗
NASA NTRS1986

Use of digital Munsell color space to assist interretation of imaging spectrometer data: Geologic examples from the northern Grapevine Mountains, California and Nevada

Techniques using Munsell color transformations were developed for reducing 128 channels (or less) of Airborne Imaging Spectrometer (AIS) data to a single color-composite-image suitable for both visual interpretation and digital analysis. Using AIS data acquired in 1984 and 1985, limestone and dolomite roof pendants and sericite-illite and other clay minerals related to alteration were mapped in a quartz monzonite stock in the northern Grapevine Mountains of California and Nevada. Field studies and laboratory spectral measurements verify the mineralogical distributions mapped from the AIS data.

Kruse, F. A.↗
NASA NTRS1986

Analysis of AIS radiometry with emphasis on determination of atmospheric properties and surface reflectance

Airborne Imaging Spectrometer data from Mono Lake, California, were studied in order to establish spectral radiance of test areas under solar illumination. The objective is to provide a method of atmospheric correction for major absorbers from the spectrometer data themselves. Crucial to the analysis is radiometric calibration of the instrument. Good agreement is found between calculated and measured radiances for uniform surface targets (beaches), but simulations of atmospheric properties with LOWTRAN lead to unreasonably low values of atmospheric precipitable water. Absorptions from carbon dioxide are not detected in the AIS data, but are strongly present in the LOWTRAN model. The apparent low contrast of all atmospheric absorption bands leads to a study of contamination from overlapping spectral orders in the AIS data. The suspected contamination is shown unambiguously to be present beyond approximately 1500 nm and consists of an extra radiance term including atmospheric bands from the delta/2 wavelength interval. A rigorous removal of the unwanted spectral contamination does not seem possible for any data taken in the rock mode. Rough estimates for tree-mode observations might be pieced together is a suitable after-the-fact radiometric claibration of the instrument can be formulated.

Conel, J. E.↗
NASA NTRS1988

Comparison of techniques for discriminating hydrothermal alteration minerals with Airborne Imaging Spectrometer data

Hydrothermal alteration mineralogy in the Tybo mining district of Nevada has been mapped on the basis of high spectral and spatial resolution Airborne Imaging Spectrometer (AIS) data, using band ratios, principal component analysis, and a signature-matching algorithm to delineate the alteration zones and limestone foundations. The signature-matching algorithm is found to be the most effective method of discriminating alteration minerals, and is noted to be able to identify mineralogy by matching AIS image spectra with library reference spectra. AIS bands in the 2048-2337-nm portion of the spectrum accounted for the greatest amount of variance.

Feldman, Sandra C.↗
DOE OSTI2024

Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment.

Yoon, Hong-Jun↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗