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At least 1,423 records · Page 79

All sources NASA NTRS DOE OSTI

NASA NTRS1973

Preparation of urban land use inventories by machine processing of ERTS MSS data

Spectral classes of urban phenomena identified from Earth Resources Technology Satellite (ERTS) multispectral scanner data in Milwaukee included suburban inner city, industry, grassy (open area), road, wooded suburb, water cloud, and shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use.

Todd, W.↗

NASA NTRS1980

Evaluating LANDSAT wildland classification accuracies

Procedures to evaluate the accuracy of LANDSAT derived wildland cover classifications are described. The evaluation procedures include: (1) implementing a stratified random sample for obtaining unbiased verification data; (2) performing area by area comparisons between verification and LANDSAT data for both heterogeneous and homogeneous fields; (3) providing overall and individual classification accuracies with confidence limits; (4) displaying results within contingency tables for analysis of confusion between classes; and (5) quantifying the amount of information (bits/square kilometer) conveyed in the LANDSAT classification.

Toll, D. L.↗

NASA NTRS1973

Digital data processing of ERTS-1 imagery of Delaware Bay

A user-oriented Earth Resources Data Center is in operation at Bendix Aerospace, which a capable of automatically producing classification imagery and thematic maps from ERTS computer compatible tapes. The ERDC is now being used to process ERTS-1 data under a subcontract from the University of Delaware. ERTS coverage of Delaware Bay has been processed to produce classification imagery of water masses in the bay, and a map of all land-water boundaries along the shoreline including wetlands and inland lakes. Imagery, the user's video monitor color display, the data processing facility, and methodology are described.

Conrod, A. C.↗

DOE OSTI2021

Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products

Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

Differentiating elements of the soil-vegetation complex

The application of remote sensing to study soil-vegetation systems requires quantification and precise location of ground observation data such that they can be correlated with multispectral aerial acquired data. Griddling and sampling of an area for exact identification in a known address on magnetic tape containing multispectral scanner data produces quantitative physico-chemical parameters and the energy which is radiating from within the soil-vegetation complex of an area. Example classifications and spectral mappings of soils made from multispectral scanner data are included.

Baumgardner, M. F.↗

NASA NTRS1974

Study of the characteristics of seismic signals generated by natural and cultural phenomena

Seismic data recorded at the Tonto Forest Seismological Observatory in Arizona and the Uinta Basin Seismological Observatory in Utah were used to compare the frequency of occurrence, severity, and spectral content of ground motions resulting from earthquakes, and other natural and man-made sources with the motions generated by sonic booms. A search of data recorded at the two observatories yielded a classification of over 180,000 earthquake phase arrivals on the basis of frequency of occurrence versus maximum ground velocity. The majority of the large ground velocities were produced by seismic surface waves from moderate to large earthquakes in the western United States, and particularly along the Pacific Coast of the United States and northern Mexico. A visual analysis of raw film seismogram data over a 3-year period indicates that local and regional seismic events, including quarry blasts, are frequent in occurrence, but do not produce ground motions at the observatories comparable to either the large western United States earthquakes or to sonic booms. Seismic data from the Nevada Test Site nuclear blasts were used to derive magnitude-distance-sonic boom overpressure relations.

Goforth, T. T.↗

NASA NTRS1989

Low-pass spatial filtering of satellite radar data

Thirty-four low-pass spatial filter treatments were applied to a multi-angle SIR-B data set to reduce speckle effects and improve classification performance. These treatments were based on four algorithms: square mean, separable mean, square median, and separable recursive median. The filtered images were evaluated using both quantitative and qualitative techniques. It was determined that the square median algorithm implemented at two iterations with a window size of 3 by 3 produced the best overall results with the 28.5-m SIR-B data.

Mueller, Paul W.↗

DOE OSTI2023

classLog: Logistic regression for the classification of genetic sequences

Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .

Zeller, Michael A.↗

NASA NTRS2007

Initial Verification of GEOS-4 Aerosols Using CALIPSO and MODIS: Scene Classification

A-train sensors such as MODIS and MISR provide column aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important because retrievals are often dependent upon selection of the right aerosol model. In addition, aerosol scene classification helps place the retrieved products in context for comparisons and analysis with aerosol transport models. The recent addition of CALIPSO to the A-train now provides a means of classifying aerosol distribution with altitude. CALIPSO level 1 products include profiles of attenuated backscatter at 532 and 1064 nm, and depolarization at 532 nm. Backscatter intensity, wavelength ratio, and depolarization provide information on the vertical profile of aerosol concentration, size, and shape. Thus similar estimates of aerosol type using MODIS or MISR are possible with CALIPSO, and the combination of data from all sensors provides a means of 3D aerosol scene classification. The NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-4) provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS-4 aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures along the flight track for NASA's Geoscience Laser Altimeter System (GLAS) satellite lidar. GLAS launched in 2003 and did not have the benefit of depolarization measurements or other sensors from the A-train. Aerosol typing from GLAS data alone was not possible, and the GEOS-4 aerosol classifier has been used to identify aerosol type and improve the retrieval of GLAS products. Here we compare 3D aerosol scene classification using CALIPSO and MODIS with the GEOS-4 aerosol classifier. Dust, smoke, and pollution examples will be discussed in the context of providing an initial verification of the 3D GEOS-4 aerosol products. Prior model verification has only been attempted with surface mass comparisons and column optical depth from AERONET and MODIS.

Welton, Ellsworth J.↗

NASA NTRS1976

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

The author has identified the following significant results. A newly developed software system for generating statistics on surface water features was tested using LANDSAT data acquired previous to 1975. This software test provided a satisfactory evaluation of the system and also allowed expansion of data base on prairie water features. The software system recognizes water on the basis of a classification algorithm. This classification is accomplished by level thresholding a single near infrared data channel. After each pixel is classified as water or nonwater, the software system then recognizes ponds or lakes as sets of contiguous pixels or as single isolated pixels in the case of very small ponds. Pixels are considered to be contiguous if they are adjacent between successive scan lines. After delineating each water feature, the software system then assigns the feature a position based upon a geographic grid system and calculates the feature's planimetric area, its perimeter, and a parameter known as the shape factor.

Gilmer, D. S.↗

NASA NTRS1975

Study of the utilization of EREP data from the Wabash River Basin

The author has identified the following significant results. Analysis of the digitized SL/4 S190A color IR photography proved very difficult. An area within Allen County, including Ft. Wayne, was studied. Eight segments of the study area were clustered separately and the cluster maps were then compared with the photography and maps available. The training areas for the land use classes were selected from the cluster maps. The separability measures (transformed divergence) of the classes indicated that many of the land use classes were not spectrally separable. The classification results bore this out. Visually the resulting classification map was poor, with 67 percent correct data. These results were of significantly lower quality than those obtained for the summertime SL/2 data near Lake Monroe, Indiana. Low contrast between land use classes during the wintertime and the limited spectral range and resolution are the major causes for the poor performance.

Silva, L. F.↗

NASA NTRS1973

Multi-discipline resource inventory of soils, vegetation and geology

The author has identified the following significant results. Computer classification of natural vegetation, in the vicinity of Big Summit Prairie, Crook County, Oregon was carried out using MSS digital data. Impure training sets, representing eleven vegetation types plus water, were selected from within the area to be classified. Close correlations were visually observed between vegetation types mapped from the large scale photographs and the computer classification of the ERTS data (Frame 1021-18151, 13 August 1972).

Simonson, G. H.↗

NASA NTRS1984

Data compression techniques for astronomy

The photometric and astrometric accuracy of the Minnesota automated dual-plate scanner is summarized. The relation between magnitude and isophotometric image diameter is linear on Schmidt plates over a large useful range. Results of a study of centroid measurement accuracy and image classification ability of parameters derived from both densitometric and isophotometric scanning techniques are presented. The data analyzed for this purpose are synthesized ideal star and galaxy brightness distributions with appropriate random noise added to simulate plate grain and sky background effects. A preliminary study of image classification using real plates and single isophote data finds that a parameter describing the raggedness of the isophote may be useful in distinguising stars from galaxies.

Landau, R.↗

NASA NTRS1980

A hill-sliding strategy for initialization of Gaussian clusters in the multidimensional space

A hill sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimate of sample data for the first step of iterative unsupervised classification. Each cluster was assumed to posses a unimodal normal distribution. A clustering function proposed distinguished elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with LANDSAT multispectral scanner data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill sliding clustering technique developed herein has the potential applicability to decomposition any multivariate mixture distribution into a number of unimodal distributions when an appropriate distribution function to the data set is employed.

Park, J. K.↗

DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗

NASA NTRS1988

Summary of along-track data from the earth radiation budget satellite for several representative ocean regions

For several days in January and August 1985, the Earth Radiation Budget Satellite, a component of the Earth Radiation Budget Experiment (ERBE), was operated in an along-track scanning mode. A survey of radiance measurements taken in this mode is given for five ocean regions: the north and south Atlantic, the Arabian Sea, the western Pacific north of the Equator, and part of the Intertropical Convergence Zone. Each overflight contains information about the clear scene and three cloud categories: partly cloudy, mostly cloudy, and overcast. The data presented include the variation of longwave and shortwave radiance in each scene classification as a function of viewing zenity angle during each overflight of one of the five target regions. Several features of interest in the development of anisotropic models are evident, including the azimuthal dependence of shortwave radiance that is an essential feature of shortwave bidirectional models. The data also demonstrate that the scene classification algorithm employed by the ERBE results in scene classifications that are a function of viewing geometry.

Brooks, David R.↗

DOE OSTI2023

Unsupervised classification for region of interest in X-ray ptychography

X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.

79 ASTRONOMY AND ASTROPHYSICS↗