Search NASA⌕ Search

SEARCH · Search NASA

Results for “Classification”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Classification of Martian deltas

Water-borne sediments in streams are deposited, upon eventual cessation of flow, either as deltas or as alluvial fans or plains. Deltas and alluvial fans share a common characteristic; both may be described as deposition Al plains at the mouth of a river or stream. A delta is formed where a stream or river deposits its sedimentary load into a standing body of water such as an ocean or lake. An alluvial fan is produced where a stream loses capacity by a greatly decreased gradient. A delta has subaerial and subaqueous components, but an alluvial fan is entirely subaerial. In terrestrial conditions, deltas and alluvial fans are reasonably distinct landforms. The juxtaposition of concomitant features composition and internal structure are sufficiently explicit as to avoid any confusion regarding their proper identification on Mars, the recognition of deltas and their distinction from alluvial fans is made difficult by low resolution imaging. Further, although it may be demonstrated that standing bodies of water existed on the surface of Mars, many of these bodies may have existed for extremely short periods of time (a few days to months); hence, distinctive shoreline features were not developed. Thus, in an attempt to derive a Martian classification of deltas, the inclusion of wholly subaerial deposits may be unavoidable. A simple, broad, morphological classification of Martian deltas, primarily on planimetric shape, includes digitate deltas, fan-shaped deltas, and re-entrant deltas. A fourth, somewhat problematical class includes featureless plains at the end of many valley systems.

Dehon, R. A.↗
NASA NTRS1993

Communications and control for electric power systems: Power flow classification for static security assessment

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Niebur, D.↗
NASA NTRS1994

A proposition for the classification of carbonaceous chondritic micrometeorites

Classification of interplanetary dust particles (IDP's) should be unambiguous and, if possible, provide an opportunity to interrelate these ultrafine IDP's with the matrices of undifferentiated meteorites. I prefer a scheme of chemical groupings and petrologic classes that is based on primary IDP properties that can be determined without prejudice by individual investigators. For IDP's of 2-50 microns these properties are bulk elemental chemistry, morphology, shape, and optical properties. The two major chemical groups are readily determined by energy dispersive spectroscopic analysis using the scanning or analytical electron microscope. Refinement of chondritic IDP classification is possible using the dominant mineral species, e.g. olivine, pyroxene, and layer silicates, and is readily inferred from FTIR, and automated chemical analysis. Petrographic analysis of phyllosilicate-rich IDP's will identify smectite-rich and serpentine-rich particles. Chondritic IDP's are also classified according to morphology, viz., CP and CF IDP's are aggregate particles that differ significantly in porosity, while the dense CS IDP's have a smooth surface. The CP IDP's are characterized by an anhydrous silicate mineralogy, but small amounts of layer silicates may be present. Distinction between the CP and CF IDP's is somewhat ambiguous, but the unique CP IDP's are fluffy, or porous, ultrafine-grained aggregates. The CP IDP's, which may contain silicate whiskers, are the most carbon-rich extraterrestrial material presently known. The CF IDP's are much less porous that CP IDP's. Using particle type definitions, CP IDP's in the NASA JSC Cosmic Dust Catalogs are approx. 15 percent of all IDP's that include nonchondritic spheres. Most aggregate particles are of the CF type.

Rietmeijer, Frans J. M.↗
NASA NTRS1994

Basalt generation at the Apollo 12 site. Part 1: New data, classification, and re-evaluation

New data are reported from five previously unanalyzed Apollo 12 mare basalts that are incorporated into an evaluation of previous petrogenetic models and classification schemes for these basalts. This paper proposes a classification for Apollo 12 mare basalts on the basis of whole-rock Mg# (molar 100*(Mg/(Mg+Fe))) and Rb/Sr ratio (analyzed by isotope dilution), whereby the ilmenite, olivine, and pigeonite basalt groups are readily distinguished from each other. Scrutiny of the Apollo 12 feldspathic 'suite' demonstrates that two of the three basalts previously assigned to this group (12031, 12038, 12072) can be reclassified: 12031 is a plagioclase-rich pigeonite basalt; and 12072 is an olivine basalt. Only basalt 12038 stands out as a unique sample to the Apollo 12 site, but whether this represents a single sample from another flow at the Apollo 12 site or is exotic to this site is equivocal. The question of whether the olivine and pigeonite basalt suites are co-magmatic is addressed by incompatible trace-element chemistry: the trends defined by these two suites when Co/Sm and Sm/Eu ratios are plotted against Rb/Sr ratio demonstrate that these two basaltic types cannot be co-magmatic. Crystal fractionation/accumulation paths have been calculated and show that neither the pigeonite, olivine, or ilmenite basalts are related by this process. Each suite requires a distinct and separate source region. This study also examines sample heterogeneity and the degree to which whole-rock analyses are representative, which is critical when petrogenetic interpretation is undertaken. Sample heterogeneity has been investigated petrographically (inhomogeneous mineral distribution) with consideration of duplicate analyses, and whether a specific sample (using average data) plots consistently upon a fractionation trend when a number of different compostional parameters are considered. Using these criteria, four basalts have been identified where reported analyses are not representative of the whole-rock composition: 12005, an ilmenite basalt; 12006 and 12036, olivine basalts; and 12031 previously classified as a feldspathic basalt, but reclassified as part of the pigeonite suite.

Neal, Clive R.↗
NASA NTRS1995

Morphology classification of galaxies in CL 0939+4713 using a ground-based telescope image

Morphological classification is studied for galaxies in cluster CL 0939+4712 at z = 0.407 using simple photometric parameters obtained from a ground-based telescope image with seeing of 1-2 arcseconds full width at half maximim (FWHM). By ploting the galaxies in a plane of the concentration parameter versus mean surface brightness, we find a good correlation between the location on the plane and galaxy colors, which are known to correlate with morphological types from a recent Hubble Space Telescope (HST) study. Using the present method, we expect a success rate of classification into early and late types of about 70% or possibly more.

Fukugita, M.↗
NASA NTRS1994

Estimation and classification by sigmoids based on mutual information

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

Baram, Yoram↗
NASA NTRS1990

On a production system using default reasoning for pattern classification

This paper addresses an unconventional application of a production system to a problem involving belief specialization. The production system reduces a large quantity of low-level descriptions into just a few higher-level descriptions that encompass the problem space in a more tractable fashion. This classification process utilizes a set of descriptions generated by combining the component hierarchy of a physical system with the semantics of the terminology employed in its operation. The paper describes an application of this process in a program, constructed in C and CLIPS, that classifies signatures of electromechanical system configurations. The program compares two independent classifications, describing the actual and expected system configurations, in order to generate a set of contradictions between the two.

Barry, Matthew R.↗
NASA NTRS1998

Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

Campbell, Janet W.↗
NASA NTRS1998

A Proposal to Develop Interactive Classification Technology

Research for the first year was oriented towards: 1) the design of an interactive classification tool (ICT); and 2) the development of an appropriate theory of inference for use in ICT technology. The general objective was to develop a theory of classification that could accommodate a diverse array of objects, including events and their constituent objects. Throughout this report, the term "object" is to be interpreted in a broad sense to cover any kind of object, including living beings, non-living physical things, events, even ideas and concepts. The idea was to produce a theory that could serve as the uniting fabric of a base technology capable of being implemented in a variety of automated systems. The decision was made to employ two technologies under development by the principal investigator, namely, SMS (Symbolic Manipulation System) and SL (Symbolic Language) [see debessonet, 1991, for detailed descriptions of SMS and SL]. The plan was to enhance and modify these technologies for use in an ICT environment. As a means of giving focus and direction to the proposed research, the investigators decided to design an interactive, classificatory tool for use in building accessible knowledge bases for selected domains. Accordingly, the proposed research was divisible into tasks that included: 1) the design of technology for classifying domain objects and for building knowledge bases from the results automatically; 2) the development of a scheme of inference capable of drawing upon previously processed classificatory schemes and knowledge bases; and 3) the design of a query/ search module for accessing the knowledge bases built by the inclusive system. The interactive tool for classifying domain objects was to be designed initially for textual corpora with a view to having the technology eventually be used in robots to build sentential knowledge bases that would be supported by inference engines specially designed for the natural or man-made environments in which the robots would be called upon to operate.

deBessonet, Cary↗
NASA NTRS1999

Dependence of Z-R Relations on the Rain Type Classification Scheme

The TRMM Global Validation Program is giving us a unique opportunity to compare radar datasets from different sites since they are analyzed in a relatively uniform procedure. Monthly Ze-R relations for four different sites (i.e, Melbourne Florida, Houston Texas, Darwin Australia and Kwajalein Atoll) were derived. The relations were obtained using the Window Probability Matching Method (WPMM). This version of the PMM relies on matching unconditional probabilities of rain rates, R, and radar reflectivity, Ze, using rain gauge and radar data, respectively. This procedure was done separately for convective and stratiform rain type using the Steiner classification procedure. The radar and gauge data from all sites were quality controlled using the same algorithms, which include also an automatic procedure to filter unreliable rain gauge data upon comparison to radar data. An adjusted power law Z-R for each rain type was also derived by comparing the radar-gauge coincident pairs in order to adjust the total monthly rainfall to match the gauges. The obtained PMM based Ze-R relations are found to be curved lines in log-log space rather than any straight line power law. While the PMM based Ze-R curves were always distinctly different between the convective and stratiform rain, the power law based Z-R, in few cases, was found to be the same for both types. In general, a given reflectivity was matched to a much lower rain intensity in the convective rainfall as compared to that in stratiform rainfall. These findings are inherently contradictory to previous findings based on disdrometer data and suggest some precaution for using the latter Z-R relations on radar data when the partition of stratiform and convective rainfall amount is in concern. The inverse trends in the relations might be caused by effects such as partial beam fillings, the use of different classification schemes, as well as having a distinct difference in the Z-R relations between the initial convective and the trailing transition regions as suggested by recent findings.

Amitai, Eyal↗
NASA NTRS1999

"Interactive Classification Technology"

The investigators are upgrading a knowledge representation language called SL (Symbolic Language) and an automated reasoning system called SMS (Symbolic Manipulation System) to enable the technologies to be used in automated reasoning and interactive classification systems. The overall goals of the project are: a) the enhancement of the representation language SL to accommodate multiple perspectives and a wider range of meaning; b) the development of a sufficient set of operators to enable the interpreter of SL to handle representations of basic cognitive acts; and c) the development of a default inference scheme to operate over SL notation as it is encoded. As to particular goals the first-year work plan focused on inferencing and.representation issues, including: 1) the development of higher level cognitive/ classification functions and conceptual models for use in inferencing and decision making; 2) the specification of a more detailed scheme of defaults and the enrichment of SL notation to accommodate the scheme; and 3) the adoption of additional perspectives for inferencing.

deBessonet, Cary↗
NASA NTRS1998

Neutral Networks for Subpixel Classification of Multispectral Images

In this work, the implementation and use of AVHRR (Advanced Very High Resolution Radiometer) images for subpixel analysis is studied. The work consists of two parts, the first is making training data, the second is using the training data to design a neural net subpixel analyzer. Most work on subpixel analysis has been done with images with more spectral bands. AVHRR images were chosen because of their easy acquisition, and because the five spectral bands allow investigation into the development of training data. The first step in subpixel analysis is the development of training data. This consists of image to be classified, and the classification of each pixel. In order to do the classification, a high spatial resolution image is typically needed in order to manually create a classified image. It is difficult to have both the image of interest, and a high spatial resolution image of the same area taken at the same time. Thus it was studied whether a subsampled image taken from the image of interest could serve as the training data. Statistical work has been done showing the unusefulness of this approach. In the second part of the analysis, a feedforward neural net was trained and used to classify the AVHRR images. Results of these tests, comparing the neural net with typical statistical based schemes are shown.

Figueroa, Ricardo R.↗
NASA NTRS1999

Robotic Rock Classification

This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.

Hebert, Martial↗
NASA NTRS2000

Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

Simard, Marc↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Physical 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 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Physical Classification Image of the SSA

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 SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. 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 NTRS2000

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

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 SSA. A Landsat-5 TM image from 02-Sep- 1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).

Hall, Forrest G.↗
NASA NTRS1994

AutoClass: A Bayesian Approach to Classification

We describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.

Stutz, John↗