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NASA NTRS1989

Classification of merged AVHRR and SMMR Arctic data with neural networks

A forward-feed back-propagation neural network is used to classify merged AVHRR and SMMR summer Arctic data. Four surface and eight cloud classes are identified. Partial memberships of each pixel to each class are examined for spectral ambiguities. Classification results are compared to manual interpretations and to those determined by a supervised maximum likelihood procedure. Results indicate that a neural network approach offers advantages in ease of use, interpretability, and utility for indistinct and time-variant spectral classes.

Key, J.↗
NASA NTRS1990

KIPSE1: A Knowledge-based Interactive Problem Solving Environment for data estimation and pattern classification

A knowledge-based interactive problem solving environment called KIPSE1 is presented. The KIPSE1 is a system built on a commercial expert system shell, the KEE system. This environment gives user capability to carry out exploratory data analysis and pattern classification tasks. A good solution often consists of a sequence of steps with a set of methods used at each step. In KIPSE1, solution is represented in the form of a decision tree and each node of the solution tree represents a partial solution to the problem. Many methodologies are provided at each node to the user such that the user can interactively select the method and data sets to test and subsequently examine the results. Otherwise, users are allowed to make decisions at various stages of problem solving to subdivide the problem into smaller subproblems such that a large problem can be handled and a better solution can be found.

Han, Chia Yung↗
NASA NTRS1990

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.

Kim, Hakil↗
NASA NTRS1989

Cloud field classification based upon high spatial resolution textural features. II - Simplified vector approaches

This paper compares the results of cloud-field classification derived from two simplified vector approaches, the Sum and Difference Histogram (SADH) and the Gray Level Difference Vector (GLDV), with the results produced by the Gray Level Cooccurrence Matrix (GLCM) approach described by Welch et al. (1988). It is shown that the SADH method produces accuracies equivalent to those obtained using the GLCM method, while the GLDV method fails to resolve error clusters. Compared to the GLCM method, the SADH method leads to a 31 percent saving in run time and a 50 percent saving in storage requirements, while the GLVD approach leads to a 40 percent saving in run time and an 87 percent saving in storage requirements.

Chen, D. W.↗
NASA NTRS1989

A practical approach to the classification of IRAS sources using infrared colors alone

Zones of the IRAS color-color planes in which a variety of different types of known source occur, have been defined for the purpose of obtaining representative IRAS colors for them. There is considerable overlap between many of these zones, rendering a unique classification difficult on the basis of IRAS colors alone, although galactic latitude can resolve ambiguities between galactic and extragalactic populations. The color dependence of these zones on the presence of spectral emission/absorption features and on the spatial extent of the sources has been investigated. It is found that silicate emission features do not significantly influence the IRAS colors. Planetary nebulae may show a dependence of color on the presence of atomic or molecular features in emission, although the dominant cause of this effect may be the underlying red continua of nebulae with strong atomic lines. Only small shifts are detected in the colors of individual spatially extended sources when total flux measurements are substituted for point-source measurements.

Walker, Helen J.↗
NASA NTRS1989

Classification of IRAS asteroids

The results of analyses of IRAS-derived asteroid albedos are presented, and their application to asteroid classification is discussed. It is noted that, while most IRAS-derived albedos are reliable, a substantial fraction of albedos lack needed corrections (e.g., for flux overestimation), and a few may be erroneous. The three-parameter taxonomic system introduced by Tedesco et al. (1989) uses only albedos based on well-observed IRAS asteroids; in this system, eleven taxonomic classes are defined on the basis of asteroid spectrum and its albedo.

Tedesco, Edward F.↗
NASA NTRS1989

Classification of solar flares

The historical background of solar flare classification before the SMM launch is reviewed along with recent developments made by observations with SMM, Hinotori, and other contemporary satellite and ground-based observations. Based on these recent findings, solar flares are grouped into five classes: thermal hard X-ray flares, nonthermal hard X-ray flares, impulsive gamma-ray/proton flares, gradual gamma-ray/proton flares, and quiescent filament-eruption flares. The roles of filament eruptions in flare development are examined, and theoretical ideas related to processes occurring in different flare classes are discussed.

Bai, T.↗
NASA NTRS1990

Classification of four ordinary chondrites from Spain

Based on optical microsocpy and electron microprobe analysis of mafic minerals, four previously poorly described ordinary chondrites from Spain are classified. The classifications of Guarena (H6), Olmedilla de Alarcon (H5) and Reliegos (L5) are confirmed. Molina is reclassified as H5, based on new data.

Mccoy, T. J.↗
NASA NTRS1990

The spectral classification of chromospherically active binary stars with composite spectra

This paper presents and analyzes blue and red-wavelength high-resolution spectra of twelve chromospherically active binary or triple systems with composite spectra. Spectral classifications for the individual stellar components are derived by means of the 'spectrum-synthesis' technique and are compared to stellar evolutionary models and observed masses and/or mass ratios. Also presented is a carefully selected set of MK reference stars of luminosity class III, IV, and V, and spectral type A9-K5, and v sin i less than 10 km/s, to cover the spectral range of the components of chromospherically active binary systems of the RS CVn-type. New values of v sin i are determined for some of the reference and program stars. Two spectroscopic binaries have been discovered.

Strassmeier, K. G.↗
NASA NTRS1990

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 NTRS1990

Segmentation, modeling and classification of the compact objects in a pile

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

Gupta, Alok↗
NASA NTRS1990

Neural network classification - A Bayesian interpretation

The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.

Wan, Eric A.↗
NASA NTRS1991

Data space volumes and classification optimization of SPOT and Landsat TM data

In order to compare the data space volume of SPOT XS and Landsat TM images, three data sets, i.e., a wetlands/agricultural data set, an agricultural data set, and a forest data set, are examined. The comparisons are made for the same geographic area. The data space volumes for Landsat TM (2, 3, and 4) are found to be 70 to 100 percent larger than the volumes for the SPOT XS images. It is suggested that the additional midinfrared bands contribute to the difference in data space volumes between Landsat TM and SPOT XS. The data space volumes for Landsat TM bands 3, 4, and 5 are more than an order of magnitude greater than the volumes for the three band SPOT XS data sets. The volumes of the six-band Landsat TM images are four orders of magnitude greater than the SPOT XS. The analysis of the data space volumes is used to optimize the computation time and minimize the storage requirements of a maximum-likelihood classification based on a look-up table.

Ahearn, Sean C.↗
NASA NTRS1989

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Gomez, Fernando↗
NASA NTRS1992

Classification Of Land Cover From Airborne MSS Data

Method for processing images of rural uplands produced by airborne multispectral scanner (MSS), semiautomatically classifies types of land cover, and involves selection of wavelength bands, radiometric calibration, correction for effects of scan angle and atmosphere, training, and assessment of accuracy. Basic version involves classification of each picture element according to spectrum. Augmented with five refinements to increase accuracy: per-field sampling; low-pass filtering; image texture; prior probabilities; and imagery from two dates.

Curran, Paul J.↗
NASA NTRS1992

Neural network classification of questionable EGRET events

High energy gamma rays (greater than 20 MeV) pair producing in the spark chamber of the Energetic Gamma Ray Telescope Experiment (EGRET) give rise to a characteristic but highly variable 3-D locus of spark sites, which must be processed to decide whether the event is to be included in the database. A significant fraction (about 15 percent or 10(exp 4) events/day) of the candidate events cannot be categorized (accept/reject) by an automated rule-based procedure; they are therefore tagged, and must be examined and classified manually by a team of expert analysts. We describe a feedforward, back-propagation neural network approach to the classification of the questionable events. The algorithm computes a set of coefficients using representative exemplars drawn from the preclassified set of questionable events. These coefficients map a given input event into a decision vector that, ideally, describes the correct disposition of the event. The net's accuracy is then tested using a different subset of preclassified events. Preliminary results demonstrate the net's ability to correctly classify a large proportion of the events for some categories of questionables. Current work includes the use of much larger training sets to improve the accuracy of the net.

Meetre, C. A.↗
NASA NTRS1991

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Stutz, John↗
NASA NTRS1992

Singularity classification as a design tool for multiblock grids

A major stumbling block in interactive design of 3-D multiblock grids is the difficulty of visualizing the design as a whole. One way to make this visualization task easier is to focus, at least in early design stages, on an aspect of the grid which is inherently easy to present graphically, and to conceptualize mentally, namely the nature and location of singularities in the grid. The topological behavior of a multiblock grid design is determined by what happens at its edges and vertices. Only a few of these are in any way exceptional. The exceptional behaviors lie along a singularity graph, which is a 1-D construct embedded in 3-D space. The varieties of singular behavior are limited enough to make useful symbology on a graphics device possible. Furthermore, some forms of block design manipulation that appear appropriate to the early conceptual-modeling phase can be accomplished on this level of abstraction. An overview of a proposed singularity classification scheme and selected examples of corresponding manipulation techniques is presented.

Jones, Alan K.↗