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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Characterization and classification of sea ice in polarimetric SAR data

A more detailed characterization of the apparent thin ice types in the multifrequency, multipolarization synthetic aperture radar (SAR) dataset acquired during a campaign for validation of the DMSP SSM/I radiometer ice products is given. The emphasis is on providing a more detailed characterization of the signatures of apparent thin ice types observed in this data and the utility of these signatures for ice type classification purposes. The statistical characteristics of these signatures and their dependence on system calibration are summarized. Implications of these observations for sea ice scattering models are briefly discussed.

Kwok, R.↗
NASA NTRS1991

Use of simulated neural networks of aerial image classification

The utility of one layer neural network in aerial image classification is examined. The network was trained with the delta rule. This method was shown to be useful as a classifier in aerial images with good resolution. It is fast, it is easy to implement, because it is distribution-free, nothing about statistical distribution of the data is needed, and it is very efficient as a boundary detector.

Medina, Frances I.↗
NASA NTRS1991

Supervised pixel classification using a feature space derived from an artificial visual system

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Baxter, Lisa C.↗
NASA NTRS1990

Myths and legends in learning classification rules

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

Buntine, Wray↗
NASA NTRS1993

General method of pattern classification using the two-domain theory

Human beings judge patterns (such as images) by complex mental processes, some of which may not be known, while computing machines extract features. By representing the human judgements with simple measurements and reducing them and the machine extracted features to a common metric space and fitting them by regression, the judgements of human experts rendered on a sample of patterns may be imposed on a pattern population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS1993

Inferring heuristic classification hierarchies from natural language input

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

Hull, Richard↗
NASA NTRS1992

Analyst variability in labeling of unsupervised classifications

Analyst variability in the labeling of unsupervised classifications is tested for Landsat 5 Thematic Mapper image products covering two test sites in southern California. The accuracy of results are tested using samples from a photo interpreted base map of the area. The significance of differences between analysts is indicated by comparing Kappa statistics derived from error matrices. Analyst variability is found to be statistically significant in most cases. Certain analysts provided consistently better results for a given study area or degree of training. This work demonstrates the potential influence of analyst bias on what would otherwise seem to be a fairly objective method and suggests that controls for this subjectivity should be factored into experimental designs.

Mcgwire, Kenneth C.↗
NASA NTRS1992

Multilayer perceptron, fuzzy sets, and classification

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Pal, Sankar K.↗
NASA NTRS1992

A system for verifying models and classification maps by extraction of information from a variety of data sources

Recent updates to a geographical information system (GIS) called VICAR (Video Image Communication and Retrieval)/IBIS are described. The system is designed to handle data from many different formats (vector, raster, tabular) and many different sources (models, radar images, ground truth surveys, optical images). All the data are referenced to a single georeference plane, and average or typical values for parameters defined within a polygonal region are stored in a tabular file, called an info file. The info file format allows tracking of data in time, maintenance of links between component data sets and the georeference image, conversion of pixel values to `actual' values (e.g., radar cross-section, luminance, temperature), graph plotting, data manipulation, generation of training vectors for classification algorithms, and comparison between actual measurements and model predictions (with ground truth data as input).

Norikane, L.↗
NASA NTRS1992

Preliminary results from the ASF/GPS ice classification algorithm

The European Space Agency Remote Sensing Satellite (ERS-1) satellite carried a C-band synthetic aperture radar (SAR) to study the earth's polar regions. The radar returns from sea ice can be used to infer properties of ice, including ice type. An algorithm has been developed for the Alaska SAR facility (ASF)/Geophysical Processor System (GPS) to infer ice type from the SAR observations over sea ice and open water. The algorithm utilizes look-up tables containing expected backscatter values from various ice types. An analysis has been made of two overlapping strips with 14 SAR images. The backscatter values of specific ice regions were sampled to study the backscatter characteristics of the ice in time and space. Results show both stability of the backscatter values in time and a good separation of multiyear and first-year ice signals, verifying the approach used in the classification algorithm.

Cunningham, G.↗
NASA NTRS1993

Si-29 NMR spectroscopy of naturally-shocked quartz from Meteor Crater, Arizona: Correlation to Kieffer's classification scheme

We have applied solid state Si-29 nuclear magnetic resonance (NMR) spectroscopy to five naturally-shocked Coconino Sandstone samples from Meteor Crater, Arizona, with the goal of examining possible correlations between NMR spectral characteristics and shock level. This work follows our observation of a strong correlation between the width of a Si-29 resonance and peak shock pressure for experimentally shocked quartz powders. The peak width increase is due to the shock-induced formation of amorphous silica, which increases as a function of shock pressure over the range that we studied (7.5 to 22 GPa). The Coconino Sandstone spectra are in excellent agreement with the classification scheme of Kieffer in terms of presence and approximate abundances of quartz, coesite, stishovite, and glass. We also observe a new resonance in two moderately shocked samples that we have tentatively identified with silicon in tetrahedra with one hydroxyl group in a densified form of amorphous silica.

Boslough, M. B.↗
NASA NTRS1993

Classificational parameters for acapulcoites and lodranites: The cases of FRO 90011, EET 84302 and ALH A81187/84190

Acapulcoites and lodranites probably sample a common parent body, which has experienced a range of partial melting. We present classificational parameters which allow acapulcoites-lodranites to be distinguished from other groups of meteorites, as well as from each other. Petrography can complement oxygen isotopic compositions in separating these meteorites from other groups of stony-irons and primitive achondrites, while petrographic properties alone distinguish acapulcoites from lodranites. Acapulcoites differ from lodranites in having smaller grain sizes, abundant Fe, Ni-FeS as micron-sized veins and plagioclase which escaped melting. We have applied these criteria to three new members of the group. FRO 90011 is a typical lodranite; EET 84302 is intermediate in many properties between acapulcoites and lodranites; and ALH A81187/84190 are paired meteorites and are first low-FeO acapulcoites. These meteorites provide a wider spectrum of samples from the acapulcoite-lodranite parent body and suggests that this body may have had a complex structure.

Mccoy, T. J.↗
NASA NTRS1993

On evaluating the accuracy of SAR sea-ice classification using multifrequency polarimetric AIRSAR data

We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.

Drinkwater, Mark R.↗
NASA NTRS1993

Automated cloud classification with a fuzzy logic expert system

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Tovinkere, Vasanth↗
NASA NTRS1993

Acoustic target detection and classification using neural networks

A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

Robertson, James A.↗
NASA NTRS1993

Classification of high dimensional multispectral image data

A method for classifying high dimensional remote sensing data is described. The technique uses a radiometric adjustment to allow a human operator to identify and label training pixels by visually comparing the remotely sensed spectra to laboratory reflectance spectra. Training pixels for material without obvious spectral features are identified by traditional means. Features which are effective for discriminating between the classes are then derived from the original radiance data and used to classify the scene. This technique is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data taken over Cuprite, Nevada in 1992, and the results are compared to an existing geologic map. This technique performed well even with noisy data and the fact that some of the materials in the scene lack absorption features. No adjustment for the atmosphere or other scene variables was made to the data classified. While the experimental results compare favorably with an existing geologic map, the primary purpose of this research was to demonstrate the classification method, as compared to the geology of the Cuprite scene.

Hoffbeck, Joseph P.↗