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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Nutrition & Dietetics↗
NASA NTRS1978

Use of LANDSAT data to assess waterfowl habitat quality

The author has identified the following significant results. The capability of mapping ponds over a very large area was demonstrated, with multidate, multiframe LANDSAT imagery. A small double sample of aircraft data made it possible to adjust a LANDSAT large area census. Terrain classification was improved by using multitemporal LANDSAT data. Waterfowl production was estimated, using remotely determined pond data, in conjunction with FWS estimates of breeding population. Relative waterfowl habitat quality was characterized on a section by section basis.

Colwell, J. E.↗
NASA NTRS1987

Characterizing forest stands with multi-incidence angle and multi-polarized SAR data

The potential for using HH-polarized L-band SAR data obtained at different incidence angles from satellite altitudes to identify and map different forest cover types and stand density classes is studied. Reasonably accurate results are obtained if the speckle characteristics of the data are suppressed by low-pass spatial filters and a contextual classification algorithm. Multipolarized L-band SAR data obtained from aircraft altitudes over the same test site are also analyzed to assess the relationships between polarization and forest stand characteristics. It is found that incidence angle controls, to a very large extent, the characteristics of the data and the type of information that can be obtained from L-band, HH-polarized satellite SAR data. Cross-polarization of L-band SAR data enhances and differentiates various forest stand characteristics which cannot be defined using only the like-polarized data, and vice-versa.

Hoffer, R. M.↗
NASA NTRS1976

Documentation of procedures for textural/spatial pattern recognition techniques

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

Haralick, R. M.↗
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS1981

LANDSAT, a data supplement to forest survey

The use of LANDSAT in providing forest data on a county basis was investigated. Image interpretation and classification techniques and their accuracy are addressed. LANDSAT data was also used to detect and delineate defoliation caused by tent caterpillars.

Thiede, G.↗
NASA NTRS1991

Progress towards SAR based ecosystem analysis

Recent progress towards a synthetic aperture radar (SAR) based system for determining forest ecosystem attributes is discussed. Our SAR data processing and analysis sequence, from calibration through classification, is described. In addition, the usefulness of SAR image data for identifying ecosystem classes is discussed.

Ranson, K. Jon↗
NASA NTRS1974

Evaluation of satellite remote sensing and automatic data techniques for characterization of wetlands and coastal marshlands

The author has identified the following significant results. ERTS digital data has been used to detect and measure accretion in the large estuarial system of the Atchafalaya River in southern Louisiana. Comparisons of aerial photography taken in October 1970 with a computer printout of ERTS digital data collected on October 1, 1972, show that in a delineated area of 1400 hectares (3460 acres) an accretion of land area in the amount of 112 hectares (277 acres) had occurred. Analysis of band 3 of the ERTS MSS was used to make a land-water map of the area. The accretion test area was marked on this map and the percentage of elements indicated as land was calculated. This percentage was then multiplied by the total test area to obtain the area of land on the date of the ERTS observation for comparison with the aerial photography. Significant improvement in classification accuracy of ERTS MSS data has been achieved by use of a priori probabilities of occurrence in pattern recognition programs. Two classifications were made with accuracies of 76.0% and 81.5%, respectively.

Cartmill, R. H.↗
NASA NTRS1980

Landsat analysis of tropical forest succession employing a terrain model

Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

Barringer, T. H.↗
NASA NTRS1977

A regional land use survey based on remote sensing and other data: A report on a LANDSAT and computer mapping project, volume 2

The author has identified the following significant results. The project mapped land use/cover classifications from LANDSAT computer compatible tape data and combined those results with other multisource data via computer mapping/compositing techniques to analyze various land use planning/natural resource management problems. Data were analyzed on 1:24,000 scale maps at 1.1 acre resolution. LANDSAT analysis software and linkages with other computer mapping software were developed. Significant results were also achieved in training, communication, and identification of needs for developing the LANDSAT/computer mapping technologies into operational tools for use by decision makers.

Nez, G.↗
NASA NTRS1981

Evaluating computer-drawn ground-cover maps

Computer-generated character maps from LANDSAT data are compared to aerial photos for test sites in Florida. Report Describes extraction of ground features by two analytical techniques: unsupervised clustering algorithm, called LANDSAT Signature Development Program (LSDP), and interactive algorithm based on multispectral image analyzer. Study concluded that computer classification of digital LANDSAT multispectral data, supplemented with certain ground-cover information, is valuable tool for analysis of renewable resources.

Arvanitis, L. G.↗
NASA NTRS1973

Crop identification and acreage measurement utilizing ERTS imagery

The author has identified the following significant results. Results of temporal overlays, equal and unequal prior probabilities, and independent test data are discussed. The amount of improvement that each technique contributed are summarized: (1) Results in Missouri where temporal overlays were made, show that temporal information improved the overall classification by 10%. (2) The dates were not optimum that were overlaid. (3) Data analysis in both Missouri and Idaho indicates that the use of prior probabilities improves the overall classification rates by at least 10% overusing the assumption that the crops are all equally likely. (4) Using both procedures together indicates that overall performance can be improved by 20% over one data and equal prior probabilities. (5) Idaho data has banding problems that may have caused serious problems in the crop classification. (6) The twelve crop types in Idaho seem to be quite similar spectrally, and hence, classification is quite difficult. (7) ERTS may not contain enough information to have perfect classification, but the data may still be useful for making crop acreage estimates. (8) Remotely sensed data could be used with a regression estimator if there is a correlation between ground data and classification results. (9) Remotely sensed data could be used with a double sample model.

Vonsteen, D. H.↗
DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗
DOE OSTI2024

Time projection chamber for GADGET II

The established Gaseous Detector with Germanium Tagging (GADGET) detection system is used to measure weak, low-energy 𝛽-delayed proton decays. It consists of the Gaseous Proton Detector equipped with a MICROMEGAS (MM) readout to detect protons and other charged particles calorimetrically, surrounded by the Segmented Germanium Array (SeGA) for high-resolution detection of prompt 𝛾 rays. To upgrade GADGET's Proton Detector to operate as a compact time projection chamber (TPC) for the detection, three-dimensional imaging and identification of low-energy 𝛽-delayed single- and multiparticle emissions mainly of interest to astrophysical studies. A new high granularity MM board with 1024 pads has been designed, fabricated, installed, and tested. A high-density data acquisition system based on generic electronics for TPCs (GET) has been installed and optimized to record and process the gas avalanche signals collected on the readout pads. The TPC's performance has been tested using a 220 Rn 𝛼-particle source and cosmic-ray muons. In addition, decay events in the TPC have been simulated by adapting the attpcroot data analysis framework. Furthermore, a novel application of two-dimensional convolutional neural networks for GADGET II event classification is introduced. The optimization of data throughput is also addressed. The GADGET II TPC is capable of detecting and identifying 𝛼 particles as well as measuring their track direction, range, and energy. The extracted energy resolution of the GADGET II TPC using P10 gas is about 5.4% at 6.288 MeV ( 220 Rn 𝛼 events), computed using charge integration. Based on a systematic simulation study, we estimated the detection efficiency of the GADGET II TPC for protons and 𝛼 particles, respectively. It has also been demonstrated that the GADGET II TPC is capable of tracking minimum-ionizing particles (i.e., cosmic-ray muons). From these measurements, the electron drift velocity was measured under typical operating conditions. In addition to being one of the first generation of micropattern gaseous detectors (MPGDs) to utilize a resistive anode applied to low-energy nuclear physics, the GADGET II TPC will also be the first TPC surrounded by a high-efficiency array of high-purity germanium 𝛾-ray detectors. As a result, the TPC of GADGET II has been designed, fabricated, and tested and is ready for operation at the Facility for Rare Isotope Beams for radioactive-beam-line experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1985

Preliminary Study of Information Extraction of LANDSAT TM Data for a Suburban/regional Test Site

A substantial amount of spectral information is available from TM (as compared to MSS) data for a 14.25 square km area between Beltsville and Laurel, Maryland. Large buildings and street patterns were resolved in the TM imagery. While there was added information content in TM data for discriminating surburban/regional land cover, characteristics of MSS can improve land cover discrimination over TM when conventional classification procedures are used on digital data. The improved qualitization of TM is likely valuable in situations where there are spectral similarities between classes. The spatial resolution in TM decreased land cover discrimination as a result of increased within class variability. For many general digital evaluations, inclusion of four bands representing the four spectral regions can provide much useful land cover discrimination. Inclusion of TM 6 indicates an improvement in spectral class discrimination. Of primary spectral importance is the discrimination between water, vegetative surfaces, and impervious surfaces due to differences in thermal properties. Results from the principle component transformed data clearly indicates additional information content in TM over MSS.

Toll, D. L.↗
NASA NTRS2001

Low Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

Yellowstone National Park (YNP) contains a diversity of land cover. YNP managers need site-specific land cover maps, which may be produced more effectively using high-resolution hyperspectral imagery. ISODATA clustering techniques have aided operational multispectral image classification and may benefit certain hyperspectral data applications if optimally applied. In response, a study was performed for an area in northeast YNP using 11 select bands of low-altitude AVIRIS data calibrated to ground reflectance. These data were subjected to ISODATA clustering and Maximum Likelihood Classification techniques to produce a moderately detailed land cover map. The latter has good apparent overall agreement with field surveys and aerial photo interpretation.

Spruce, Joe↗
NASA NTRS2017

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Subpixel↗