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NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweetÂ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's MultiÂ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗

NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗

NASA NTRS2009

NASA Lunar Mining and Construction Activities and Plans

The Space Exploration Policy enacted by the US Congress in 2005 calls for the US National Aeronautics and Space Administration (NASA) to implement a sustained and affordable human and robotic program to explore the solar system and beyond; Extend human presence across the solar system, starting with a human return to the Moon by the year 2020, in preparation for human exploration of Mars and other destinations; Develop the innovative technologies, knowledge, and infrastructures both to explore and to support decisions about the destinations for human exploration; and Promote international and commercial participation in exploration to further U.S. scientific, security, and economic interests. In 2006, NASA released the Lunar Architecture Study, which proposed establishing a lunar Outpost on the Moon with international participation to extend human presence beyond Earth's orbit, pursue scientific activities, use the Moon to prepare for future human missions to Mars, and expand Earth s economic sphere. The establishment of sustained human presence on the Moon for science and exploration combines the design, integration, and operation challenges experienced from both the short Apollo lunar missions and the build-up and sustained crew operations of the International Space Station (ISS). Apollo experience reminds developers and mission planners that hardware must operate under extremely harsh environmental and abrasive conditions and every kilogram of mass and payload must be critical to achieve the mission s objectives due to the difficulty and cost of reaching the lunar surface. Experience from the ISS reminds developers and mission planners that integration of all hardware must be designed and planned from the start of the program, operations and evolution of capabilities on a continuous basis are important, and long-term life-cycle costs and logistical needs are equally or more important than minimizing early development and test costs. Overarching all of this is the need to implement efforts that are sustainable and affordable. One area NASA is developing that can significantly change how systems required for sustained human presence are designed and integrated, as well as potentially break our reliance on Earth supplied logistics, is In-Situ Resource Utilization (ISRU). ISRU, also known living off the land, involves the extraction and processing of local resources into useful products. In particular, the ability to make propellants, life support consumables, fuel cell reagents, and radiation shielding can significantly reduce the cost, mass, and risk of sustained human activities beyond Earth. Also, the ability to modify the lunar landscape for safer landing, transfer of payloads from the lander an outpost, dust generation mitigation, and infrastructure placement and buildup are also extremely important for long-term lunar operations. While extra-terrestrial excavation, material handling and processing, and site preparation and construction may be new to NASA and other space agencies, there is extensive terrestrial hardware and commercial experience that can be leveraged. This paper will provide an overview of current NASA activities in lunar ISRU mining and construction and how terrestrial experience in these areas are important to achieving the goal of affordable and sustainable human exploration.

Sanders, Gerald B.↗

NASA NTRS1991

Systems engineering studies of lunar base construction

Many ingenious concepts have been proposed for lunar base construction, but few systematic studies exist which relate time-consistent lunar base construction technologies and the choice of lunar base approach with the long-term SEI objectives - i.e., lunar indigenous base construction and Mars Exploration equipment development. To fill this gap, CSC has taken a two-pronged approach. First, the Center undertook basic geotechnical investigations of lunar soil, fabrication of a scale prototype of a lunar construction crane, a multi-robot construction team laboratory experiment, and a preliminary design of lunar base structures. Second, during Jun. and Jul. 1991 two lunar base construction systems engineering studies were accomplished - a 'near term lunar base' study, and a 'far-term lunar base' study. The goals of these studies were to define the major lunar base construction research problems in consistent technology/construction frameworks, and to define design requirements for construction equipment such as a lunar crane and a regolith mover. The 'near-term lunar base' study examined three different construction concepts for a lunar base comprised of pre-fabricated, pre-tested, Space Station Freedom-type modules, which would be covered with regolith shielding. Concept A used a lunar crane for unloading and transportation; concept B, a winch and cart; and concept C, a walker to move the modules from the landing site to the base site and assemble them. To evaluate the merits of each approach, calculations were made of mass efficiency measure, source mass, reliability, far-term base mass, Mars base mass, and base assembly time. The model thus established was also used to define the requirements for crane speed and regolith mover m(sup 3)/sec rates. A major problem addressed is how to 'mine' the regolith and stack it over the habitats as shielding. To identify when the cost of using indigenous lunar materials to construct the base exceeds the cost of development and delivery of the equipment for processing lunar materials, a study of construction of a candidate sintered regolith 'far term lunar base' was undertaken. A technique was devised for casting slabs of sintered (basaltic) regolith and assembling these into a hemispherical (or geodesic) dome. The major problem occurs with the inner liner. At 14.7 psi and 20 percent oxygen internal atmosphere, the entire structure is in tension, even with the regolith load. Also, another study has indicated that at 14.7 psi major resupply of air will be needed because of leakage, and astronauts may have to engage in extensive pre-breathing and post-breathing for extravehicular activity (EVA) tasks, thus detracting from useful mission work time. An alternative is to operate part of the base at, say, 5 psi and 70 percent oxygen, or to equip the astronauts with hard suits at 8.3 psi or greater. All of these choices directly influence base design and construction techniques.

Morgenthaler, George W.↗

NASA NTRS2014

Design Visualization Internship Overview

This is a report documenting the details of my work as a NASA KSC intern for the Summer Session from June 2nd to August 8th, 2014. This work was conducted within the Design Visualization Group, a Contractor staffed organization within the C1 division of the IT Directorate. The principle responsibilities of the KSC Design Visualization Group are the production of 3D simulations of NASA equipment and facilities for the purpose of planning complex operations such as hardware transportation and vehicle assembly. My role as an intern focused on aiding engineers in using 3D scanning equipment to obtain as-built measurements of NASA facilities, as well as using CATIA and DELMIA to process this data. My primary goals for this internship focused on expanding my CAD knowledge and capabilities, while also learning more about technologies I was previously unfamiliar with, such as 3D scanning. An additional goal of mine was to learn more about how NASA operates, and how the U.S. Space Program operates on a day-to-day basis. This opportunity provided me with a front-row seat to the daily maneuvers and operations of KSC and NASA as a whole. Each work day, I was able to witness, and even take part of, a small building block of the future systems that will take astronauts to other worlds. After my experiences this summer, not only can I say that my goals have been met, but also that this experience has been the highlight of my experience in higher education.

Telescience↗

NASA NTRS2010

Stereoscopic Machine-Vision System Using Projected Circles

A machine-vision system capable of detecting obstacles large enough to damage or trap a robotic vehicle is undergoing development. The system includes (1) a pattern generator that projects concentric circles of laser light forward onto the terrain, (2) a stereoscopic pair of cameras that are aimed forward to acquire images of the circles, (3) a frame grabber and digitizer for acquiring image data from the cameras, and (4) a single-board computer that processes the data. The system is being developed as a prototype of machine- vision systems to enable robotic vehicles ( rovers ) on remote planets to avoid craters, large rocks, and other terrain features that could capture or damage the vehicles. Potential terrestrial applications of systems like this one could include terrain mapping, collision avoidance, navigation of robotic vehicles, mining, and robotic rescue. This system is based partly on the same principles as those of a prior stereoscopic machine-vision system in which the cameras acquire images of a single stripe of laser light that is swept forward across the terrain. However, this system is designed to afford improvements over some of the undesirable features of the prior system, including the need for a pan-and-tilt mechanism to aim the laser to generate the swept stripe, ambiguities in interpretation of the single-stripe image, the time needed to sweep the stripe across the terrain and process the data from many images acquired during that time, and difficulty of calibration because of the narrowness of the stripe. In this system, the pattern generator does not contain any moving parts and need not be mounted on a pan-and-tilt mechanism: the pattern of concentric circles is projected steadily in the forward direction. The system calibrates itself by use of data acquired during projection of the concentric-circle pattern onto a known target representing flat ground. The calibration- target image data are stored in the computer memory for use as a template in processing terrain images. During operation on terrain, the images acquired by the left and right cameras are analyzed. The analysis includes (1) computation of the horizontal and vertical dimensions and the aspect ratios of rectangles that bound the circle images and (2) comparison of these aspect ratios with those of the template. Coordinates of distortions of the circles are used to identify and locate objects. If the analysis leads to identification of an object of significant size, then stereoscopicvision algorithms are used to estimate the distance to the object. The time taken in performing this analysis on a single pair of images acquired by the left and right cameras in this system is a fraction of the time taken in processing the many pairs of images acquired in a sweep of the laser stripe across the field of view in the prior system. The results of the analysis include data on sizes and shapes of, and distances and directions to, objects. Coordinates of objects are updated as the vehicle moves so that intelligent decisions regarding speed and direction can be made. The results of the analysis are utilized in a computational decision-making process that generates obstacle-avoidance data and feeds those data to the control system of the robotic vehicle.

Mackey, Jeffrey R.↗

NASA NTRS2004

Aluminum-Alloy-Matrix/Alumina-Reinforcement Composites

Isotropic composites of aluminum-alloy matrices reinforced with particulate alumina have been developed as lightweight, high-specific-strength, less-expensive alternatives to nickel-base and ferrous superalloys. These composites feature a specific gravity of about 3.45 grams per cubic centimeter and specific strengths of about 200 MPa/(grams per cubic centimeter). The room-temperature tensile strength is 100 ksi (689 MPa) and stiffness is 30 Msi (206 GPa). At 500 F (260 C), these composites have shown 80 percent retention in strength and 95 percent retention in stiffness. These materials also have excellent fatigue tolerance and tribological properties. They can be fabricated in net (or nearly net) sizes and shapes to make housings, pistons, valves, and ducts in turbomachinery, and to make structural components of such diverse systems as diesel engines, automotive brake systems, and power-generation, mining, and oil-drilling equipment. Separately, incorporation of these metal matrix composites within aluminum gravity castings for localized reinforcement has been demonstrated. A composite part of this type can be fabricated in a pressure infiltration casting process. The process begins with the placement of a mold with alumina particulate preform of net or nearly net size and shape in a crucible in a vacuum furnace. A charge of the alloy is placed in the crucible with the preform. The interior of the furnace is evacuated, then the furnace heaters are turned on to heat the alloy above its liquidus temperature. Next, the interior of the furnace is filled with argon gas at a pressure about 900 psi (approximately equal to 6.2 MPa) to force the molten alloy to infiltrate the preform. Once infiltrated, the entire contents of the crucible can be allowed to cool in place, and the composite part recovered from the mold.

Kashalikar, Uday↗

NASA NTRS1980

Evaluation of multispectral middle infrared aircraft images for lithologic mapping in the East Tintic Mountains, Utah

Six channels of multispectral middle infrared (8 to 14 micron) aircraft scanner data were acquired over the East Tintic mining district, Utah. This area has high relief and moderate vegetation and consists mainly of Tertiary silicic igneous rocks and Paleozoic quartzite and carbonate rocks that have been locally hydrothermally altered. These digital-image data were computer processed to create a color-composite image based on principal component transformations. Color differences in this image are related to the spectral differences in the surface material and allow discrimination of several rock types, depending primarily on their silica content. When combined with a visible and near infrared color-composite image from a previous flight, with limited field checking, it is possible to discriminate quartzite, carbonate rocks, quartz latitic and quartz monzonitic rocks, latitic and monzonitic rocks, silicified altered rocks, argillized altered rocks, and vegetation.

Kahle, A. B.↗

NASA NTRS1989

A robotically constructed production and supply base on Phobos

PHOBIA Corporation is involved with the design of a man-tenable robotically constructed, bootstrap base on Mars' moon, Phobos. This base will be a pit-stop for future manned missions to Mars and beyond and will be a control facility during the robotic construction of a Martian base. An introduction is given to the concepts and the ground rules followed during the design process. Details of a base design and its location are given along with information about some of the subsystems. Since a major purpose of the base is to supply fuel to spacecraft so they can limit their fuel mass, mining and production systems are discussed. Surface support activities such as docks, anchors, and surface transportation systems are detailed. Several power supplies for the base are investigated and include fuel cells and a nuclear reactor. Tasks for the robots are defined along with descriptions of the robots capable of completing the tasks. Finally, failure modes for the entire PHOBIA Corporation design are presented along with an effects analysis and preventative recommendations.

Source record↗

NASA NTRS2003

CO2 Acquisition Membrane (CAM) Project

The CO2 Acquisition Membrane (CAM) project was performed to develop, test, and analyze thin film membrane materials for separation and purification of carbon dioxide (CO2) from mixtures of gases, such as those found in the Martian atmosphere. The membranes developed in this project are targeted toward In Situ Resource Utilization (ISRU) applications, such as In Situ Propellant Production (ISPP) and In Situ Consumables Production (ISCP). These membrane materials may be used in a variety of ISRU systems, for example as the atmospheric inlet filter for an ISPP process to enhance the concentration of CO2 for use as a reactant gas, to passively separate argon and nitrogen trace gases from CO2 for habitat pressurization, to provide a system for removal of CO2 from breathing gases in a closed environment, or within a process stream to selectively separate CO2 from other gaseous components. The membranes identified and developed for CAM were evaluated for use in candidate ISRU processes and other gas separation applications, and will help to lay the foundation for future unmanned sample return and human space missions. CAM is a cooperative project split among three institutions: Lockheed Martin Astronautics (LMA), the Colorado School of Mines (CSM), and Marshall Space Flight Center (MSFC).

Mason, Larry W.↗

NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Miller, Jerry↗

NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗

NASA NTRS2005

Challenges in Predicting Planetary Granular Mechanics

Through the course of human history, our needs in agriculture, habitat construction, and resource extraction have driven us to gain more experience working with the granular materials of planet Earth than with any other type of substance in nature, with the possible exception being water. Furthermore, throughout the past two centuries we have seen a dramatic and ever growing interest among scientists and engineers to understand and predict both its static and rheological properties. Ironically, however, despite this wealth of experience we still do not have a fundamental understanding of the complex physical phenomena that emerge even as just ordinary sand is shaken, squeezed or poured. As humanity is now reaching outward through the solar system, not only robotic ally but also with our immediate human presence, the need to understand and predict granular mechanics has taken on a new dimension. We must learn to farm, build and mine the regoliths of other planets where the environmental conditions are different than on Earth, and we are rapidly discovering that the effects of these environmental conditions are not trivial. Some of the relevant environmental features include the regolith formation processes throughout a planet's geologic and hydrologic history, the unknown mixtures of volatiles residing within the soil, the relative strength of gravitation, ~d the atm9spheric pressure and its seasonal variations. The need to work with soils outside our terrestrial experience base provides us with both a challenge and an opportunity. The challenge is to learn how to extrapolate our experience into these new planetary conditions, enabling the engineering decisions that are needed right now as we take the next few steps in solar system exploration. The opportunity is to use these new planetary environments as laboratories that will help us to see granular mechanics in new ways, to challenge our assumptions, and to help us finally unravel the elusive physics that lie behind complex granular phenomena. Toward these goals, a workshop was held recently at NASA's John F. Kennedy Space Center, attracting over a hundred scientists and engineers from around the world and from a broad crosssection of scientific and engineering disciplines. This talk will provide an out-briefing from that workshop, communicating some of its early findings in regard to lunar and Martian exploration: (1) the requirements for working with granular materials, (2) the challenges that granular materials will pose, (3) the environmental conditions that affect granular mechanics, (4) instruments and measurements that are needed on the Moon and Mars to support granular material research, and (5) some of the possible research avenues that should be pursued.

Metzger, Philip T.↗

NASA NTRS1992

Lunar exploration for resource utilization

The strategy for developing resources on the Moon depends on the stage of space industrialization. A case is made for first developing the resources needed to provide simple materials required in large quantities for space operations. Propellants, shielding, and structural materials fall into this category. As the enterprise grows, it will be feasible to develop additional sources - those more difficult to obtain or required in smaller quantities. Thus, the first materials processing on the Moon will probably take the abundant lunar regolith, extract from it major mineral or glass species, and do relatively simple chemical processing. We need to conduct a lunar remote sensing mission to determine the global distribution of features, geophysical properties, and composition of the Moon, information which will serve as the basis for detailed models of and engineering decisions about a lunar mine.

Duke, Michael B.↗

NASA NTRS1975

Case studies

The characteristics of the technology assessment process which were manifested in response to the hazardous effects of early medical uses of X-rays are considered. Other topics discussed include: controlling the potential hazards of government-sponsored technology, genetic technology, community level impacts of expanded underground coal mining, and an integrated strategy for aircraft/airport noise abatement.

Source record↗

NASA NTRS2015

When the Sky Falls: Performing Initial Assessments of Bright Atmospheric Events

The 2013 Chelyabinsk super bolide was the first "significant" impact event to occur in the age of social media and 24 hour news. Scientists, used to taking many days or weeks to analyze fireball events, were hard pressed to meet the immediate demands (within hours) for answers from the media, general public, and government officials. Fulfilling these requests forced many researchers to exploit information available from various Internet sources - videos were downloaded from sites like Youtube, geolocated via Google Street View, and quickly analyzed with improvised software; Twitter and Facebook were scoured for eyewitness accounts of the fireball and reports of meteorites. These data, combined with infrasound analyses, enabled a fairly accurate description of the Chelyabinsk event to be formed within a few hours; in particular, any relationship to 2012 DA14 (which passed near Earth later that same day) was eliminated. Results of these analyses were quickly disseminated to members of the NEO community for press conferences and media interviews. Despite a few minor glitches, the rapid initial assessment of Chelyabinsk was a triumph, permitting the timely conveyance of accurate information to the public and the incorporation of social media into fireball analyses. Beginning in 2008, the NASA Meteoroid Environments Office, working in cooperation with Western's Meteor Physics Group, developed processes and software that permit quick characterization - mass, trajectory, and orbital properties - of fireball events. These tools include automated monitoring of Twitter to establish the time of events (the first tweet is usually no more than a few seconds after the fireball), mining of Youtube and all sky camera web archives to locate videos suitable for analyses, use of Google Earth and Street View to geolocate the video locations, and software to determine the fireball trajectory and object orbital parameters, including generation of animations suitable for popular media and presentations. This presentation will give a short description of the characterization procedure and show applications of the tools, which have become vital to answering the question of "What was that bright light in the sky?" in the post-Chelyabinsk, 24/7 news world.

Cooke, William J.↗

NASA NTRS2023

Data Mining for Science of the Sun-Earth Connection as a Single System

Establishing the Sun-Earth connection requires overcoming the challenges of exploring the data from past and current missions and leveraging tools and models (data mining) to create an efficient system treatment of the Sun and heliosphere. However, solar and heliospheric environment data constitute a vast source of information whose potential is far from being optimally exploited. In the next decade, the solar and heliospheric community will have to manage the increasing amount of information coming from new missions, improve reanalysis of data from past and current missions, and create new data products from the application of new methodologies. This complex task is further complicated by practical challenges such as different datasets and catalogs in different formats that may require different pre-processing and analysis tools, and the need for numerous analysis approaches that are not all fully optimized for large volumes of data. While several ongoing efforts aim at addressing these problems, the available datasets and tools are not always used to their full potential often due to lack of awareness of available resources. In this paper, we summarize the issues raised and goals discussed by members of the community during recent conference sessions focused on data mining for science.

Sun-Earth connection↗

NASA NTRS2002

Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering

This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.

Tilton, James C.↗