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NASA NTRS1998

Incorporating the APS Catalog of the POSS I and Image Archive in ADS

The primary purpose of this contract was to develop the software to both create and access an on-line database of images from digital scans of the Palomar Sky Survey. This required modifying our DBMS (called Star Base) to create an image database from the actual raw pixel data from the scans. The digitized images are processed into a set of coordinate-reference index and pixel files that are stored in run-length files, thus achieving an efficient lossless compression. For efficiency and ease of referencing, each digitized POSS I plate is then divided into 900 subplates. Our custom DBMS maps each query into the corresponding POSS plate(s) and subplate(s). All images from the appropriate subplates are retrieved from disk with byte-offsets taken from the index files. These are assembled on-the-fly into a GIF image file for browser display, and a FITS format image file for retrieval. The FITS images have a pixel size of 0.33 arcseconds. The FITS header contains astrometric and photometric information. This method keeps the disk requirements manageable while allowing for future improvements. When complete, the APS Image Database will contain over 130 Gb of data. A set of web pages query forms are available on-line, as well as an on-line tutorial and documentation. The database is distributed to the Internet by a high-speed SGI server and a high-bandwidth disk system. URL is http://aps.umn.edu/IDB/. The image database software is written in perl and C and has been compiled on SGI computers with MIX5.3. A copy of the written documentation is included and the software is on the accompanying exabyte tape.

Humphreys, Roberta M.↗

DOE OSTI2021

Hierarchical median narrow band for level set segmentation of cervical cell nuclei

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. Furthermore, these results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.

47 OTHER INSTRUMENTATION↗

NASA NTRS1993

Operator Performance Support System (OPSS)

In the complex and fast reaction world of military operations, present technologies, combined with tactical situations, have flooded the operator with assorted information that he is expected to process instantly. As technologies progress, this flow of data and information have both guided and overwhelmed the operator. However, the technologies that have confounded many operators today can be used to assist him -- thus the Operator Performance Support Team. In this paper we propose an operator support station that incorporates the elements of Video and Image Databases, productivity Software, Interactive Computer Based Training, Hypertext/Hypermedia Databases, Expert Programs, and Human Factors Engineering. The Operator Performance Support System will provide the operator with an integrating on-line information/knowledge system that will guide expert or novice to correct systems operations. Although the OPSS is being developed for the Navy, the performance of the workforce in today's competitive industry is of major concern. The concepts presented in this paper which address ASW systems software design issues are also directly applicable to industry. the OPSS will propose practical applications in how to more closely align the relationships between technical knowledge and equipment operator performance.

Conklin, Marlen Z.↗

NASA NTRS1997

The Contribution of Ionizing Stars to the Far-Infrared and Radio Emission in the Galaxy

This is the first report of a new contract. However, this project represents ongoing work, so there are completed tasks as well as newly started tasks to report. The project involves the completion of the IRAS Galaxy Atlas (IGA), a large image database produced using data from the Infrared Astronomical Satellite (IRAS). In this phase, the project switches from the production and characterization of the IGA to its use in astronomical research studies of massive star formation. The research utilizes the IGA as well as two other large data sets being produced by research partners.

Cao, Yu↗

NASA NTRS1996

Perceptual Image Compression in Telemedicine

The next era of space exploration, especially the "Mission to Planet Earth" will generate immense quantities of image data. For example, the Earth Observing System (EOS) is expected to generate in excess of one terabyte/day. NASA confronts a major technical challenge in managing this great flow of imagery: in collection, pre-processing, transmission to earth, archiving, and distribution to scientists at remote locations. Expected requirements in most of these areas clearly exceed current technology. Part of the solution to this problem lies in efficient image compression techniques. For much of this imagery, the ultimate consumer is the human eye. In this case image compression should be designed to match the visual capacities of the human observer. We have developed three techniques for optimizing image compression for the human viewer. The first consists of a formula, developed jointly with IBM and based on psychophysical measurements, that computes a DCT quantization matrix for any specified combination of viewing distance, display resolution, and display brightness. This DCT quantization matrix is used in most recent standards for digital image compression (JPEG, MPEG, CCITT H.261). The second technique optimizes the DCT quantization matrix for each individual image, based on the contents of the image. This is accomplished by means of a model of visual sensitivity to compression artifacts. The third technique extends the first two techniques to the realm of wavelet compression. Together these two techniques will allow systematic perceptual optimization of image compression in NASA imaging systems. Many of the image management challenges faced by NASA are mirrored in the field of telemedicine. Here too there are severe demands for transmission and archiving of large image databases, and the imagery is ultimately used primarily by human observers, such as radiologists. In this presentation I will describe some of our preliminary explorations of the applications of our technology to the special problems of telemedicine.

Watson, Andrew B.↗

NASA NTRS2003

Global Characterization of Polygonally Fractured Terrain on Venus and Implications for a Climate Change Origin

On Earth, polygonal fractures with spacings of 1-100 cm form in lava flows that cool slowly under isotropic stress conditions. On Venus, polygonal fractures are observed with typical fracture spacings of 1-2 km, covering regions 10s to 100s of km across. These features have been proposed to form via cooling of lava flows or above subsurface intrusions, or due to surface cooling caused by climate change. A watershed algorithm was modified to search the entire Magellan image database autonomously for terrains with polygonal fractures. Here we characterize the 204 polygon terrains found using this method (including over 170 newly identified regions) with respect to size, morphology, stratigraphy, and geologic setting. These results are used to examine different models of origin.

Smrekar, S. E.↗

NASA NTRS2019

PERISCOPE: PERIapsis Subsurface Cave Optical Explorer

The PERISCOPE study focuses primarily on lunar caves, due to the potential for being imaged in orbital scenarios. In the intervening years, from 2012-2015, scientists developed further rationales and interest in the scientific value of lunar caves. It does not appear that they are likely to be sinks for water-ice due to the relatively warm temperatures(~-20 degrees Celsius) in the caves leading to geologically-rapid migration of unbound water due to sublimation, and inevitable loss through any skylights. However, the skylights themselves reveal apparent complex layering, which may speak to a more complex multi-stage evolution of mare flood basalts than previously considered, and so their examination may provide even more insight into the lunar mare, which in turn provide a primary record of early solar system crustal formal and evolution processes. Further extrapolation of these insights can be found within the exoplanet community of researchers,who find the information useful for calibrating star formation and planetary evolution models. In addition, catalogues of lunar and martian skylights, "caves" or "atypical pit craters" have been developed, with numbers for both bodies now in the low hundreds thanks to additional high resolution surveys and revisiting the existing image databases.

Skylights↗

NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗

DOE OSTI2020

Characterizing natural fractures and sub-seismic faults for well completion of Marcellus shale in the MSEEL Consortium project, West Virginia, USA

The Middle Devonian Marcellus shale play has emerged as a major world-class hydrocarbon accumulation and represents one of the largest and most prolific shale plays in the world. According to many outcrop studies in the region, natural fractures are well developed in the Marcellus Shale. However, evaluating fractures in the subsurface is often a significant challenge due to a lack of sufficient data. Therefore, in the Marcellus Shale Energy and Environment Laboratory (MSEEL) consortium project, significant efforts have been made to acquire high-quality image logs in the Marcellus laterals. The project provided tremendous opportunities to characterize the natural fractures and sub-seismic faults and to evaluate their impact on well stimulation. In this study, about 70,000 ft of acquired high-resolution logging while drilling (LWD) acoustic images from five long laterals located in Monongalia County, West Virginia, were processed and interpreted. In addition, the study used high-quality micro-resistivity images from a pilot well, allowing the evaluation of natural fractures in the entire Marcellus vertical sequence. Based on the available acoustic images, the natural fractures were classified into three basic categories: high-amplitude fractures, low-amplitude fractures, and faults. Further, larger open fractures can also be determined when a low-amplitude fracture is evident on caliper images. The fractures in the Marcellus usually have a medium to high angle dip; however, multiple fracture sets in terms of strike orientation were clearly observed in all the laterals. The fracture set with a strike at NE-SW (or 60-240 deg) seems to be the predominant one in all the wells. A few other sets, including those with N-S, NWW-SEE, and E-W strikes, were also observed. Several sub-seismic faults, with mostly a low dip angle and a NE-SW strike, have also been seen in two of the laterals. The fracture density is variable across all the laterals, ranging from very low (or none) to very high (up to 5 fractures per ft). The average fracture density for all the laterals is about 1 fracture per 10 ft. In the vertical sequence, the natural fracture development showed a clear preference for shale or shaly facies over carbonate-rich or thin limestone layers. The interpreted fracture and fault data were used as input data for the stimulation design with the purpose of better understanding the fractures’ impact on well stimulation. Production data from the laterals were also used to evaluate the natural fractures’ influence on well performance. The quality image database and the consistent interpretation results for the entire project enabled a systematic approach to characterizing fractures and, more importantly, to evaluating the impact of fractures on well stimulation and production.

03 NATURAL GAS↗

NASA NTRS1991

A complete database for the Einstein imaging proportional counter

A complete database for the Einstein Imaging Proportional Counter (IPC) was completed. The original data that makes up the archive is described as well as the structure of the database, the Op-Ed analysis system, the technical advances achieved relative to the analysis of (IPC) data, the data products produced, and some uses to which the database has been put by scientists outside Columbia University over the past year.

Helfand, David J.↗

NASA NTRS2012

Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

Wolf, Michael↗

NASA NTRS1996

A Visual Database System for Image Analysis on Parallel Computers and its Application to the EOS Amazon Project

The goal of this task was to create a design and prototype implementation of a database environment that is particular suited for handling the image, vision and scientific data associated with the NASA's EOC Amazon project. The focus was on a data model and query facilities that are designed to execute efficiently on parallel computers. A key feature of the environment is an interface which allows a scientist to specify high-level directives about how query execution should occur.

Shapiro, Linda G.↗

NASA NTRS2002

High Resolution Soil Water from Regional Databases and Satellite Images

This viewgraph presentation provides information on the ways in which plant growth can be inferred from satellite data and can then be used to infer soil water. There are several steps in this process, the first of which is the acquisition of data from satellite observations and relevant information databases such as the State Soil Geographic Database (STATSGO). Then probabilistic analysis and inversion with the Bayes' theorem reveals sources of uncertainty. The Markov chain Monte Carlo method is also used.

Morris, Robin D.↗

NASA NTRS2013

Space Images for NASA JPL Android Version

This software addresses the demand for easily accessible NASA JPL images and videos by providing a user friendly and simple graphical user interface that can be run via the Android platform from any location where Internet connection is available. This app is complementary to the iPhone version of the application. A backend infrastructure stores, tracks, and retrieves space images from the JPL Photojournal and Institutional Communications Web server, and catalogs the information into a streamlined rating infrastructure. This system consists of four distinguishing components: image repository, database, server-side logic, and Android mobile application. The image repository contains images from various JPL flight projects. The database stores the image information as well as the user rating. The server-side logic retrieves the image information from the database and categorizes each image for display. The Android mobile application is an interfacing delivery system that retrieves the image information from the server for each Android mobile device user. Also created is a reporting and tracking system for charting and monitoring usage. Unlike other Android mobile image applications, this system uses the latest emerging technologies to produce image listings based directly on user input. This allows for countless combinations of images returned. The backend infrastructure uses industry-standard coding and database methods, enabling future software improvement and technology updates. The flexibility of the system design framework permits multiple levels of display possibilities and provides integration capabilities. Unique features of the software include image/video retrieval from a selected set of categories, image Web links that can be shared among e-mail users, sharing to Facebook/Twitter, marking as user's favorites, and image metadata searchable for instant results.

Nelson, Jon D.↗

NASA NTRS1987

Digital cartography of the planets - New methods, its status, and its future

A system has been developed that establishes a standardized cartographic database for each of the 19 planets and major satellites that have been explored to date. Compilation of the databases involves both traditional and newly developed digital image processing and mosaicking techniques, including radiometric and geometric corrections of the images. Each database, or digital image model (DIM), is a digital mosaic of spacecraft images that have been radiometrically and geometrically corrected and photometrically modeled. During compilation, ancillary data files such as radiometric calibrations and refined photometric values for all camera lens and filter combinations and refined camera-orientation matrices for all images used in the mapping are produced. The system is designed to aid in the following: access to and utilization of spatial data returned by planetary spacecraft; coregistration of cartographic image and topographic elevation models with geological, geophysical, and geochemical databases; and the design and preparation of both standard and custom map products. A major goal of the project is to publish the DIMs on compact computer-compatible media such as digital-optical (CD-ROM) disks.

Batson, R. M.↗

NASA NTRS2018

Towards Informing an Intuitive Mission Planning Interface for Autonomous Multi-Asset Teams via Image Descriptions

Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Human-Machine Interface (HMI) team is working to capture and utilize the multitude of ways in which humans are already comfortable communicating mission goals and translate that into an intuitive mission planning interface. Several input/output modalities (speech/audio, typing/text, touch, and gesture) are being considered and investigated in the context human-machine teaming for the ATTRACTOR design reference mission (DRM) of Search and Rescue or (more generally) intelligence, surveillance, and reconnaissance (ISR). The first of these investigations, the Human Informed Natural-language GANs Evaluation (HINGE) data collection effort, is aimed at building an image description database to train a Generative Adversarial Network (GAN). In addition to building an image description database, the HMI team was interested if, and how, modality (spoken vs. written) affects different aspects of the image description given. The results will be analyzed to better inform the designing of an interface for mission planning.

Generative Adversarial Network (GAN)↗

NASA NTRS1989

Design considerations for a space database

Part of the information used in a real-time simulator is stored in the visual database. This information is processed by an image generator and displayed as a real-time visual image. The database must be constructed in a specific format, and it should efficiently utilize the capacities of the image generator that is was created for. A visual simulation is crucially dependent upon the success with which the database provides visual cues and recognizable scenes. For this reason, more and more attention is being paid to the art and science of creating effective real-time visual databases. Investigated here are the database design considerations required for a space-oriented real-time simulator. Space applications often require unique designs that correspond closely to the particular image-generator hardware and visual-database-management software. Specific examples from the databases constructed for NASA and its Evans and Sutherland CT6 image generator illustrate the various design strategies used in a space-simulation environment. These database design considerations are essential for all who would create a space database.

Moss, Lance M.↗

DOE OSTI2021

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities.

14 SOLAR ENERGY↗