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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Analyzing High-Dimensional Multispectral Data

In this paper, through a series of specific examples, we illustrate some characteristics encountered in analyzing high- dimensional multispectral data. The increased importance of the second-order statistics in analyzing high-dimensional data is illustrated, as is the shortcoming of classifiers such as the minimum distance classifier which rely on first-order variations alone. We also illustrate how inaccurate estimation or first- and second-order statistics, e.g., from use of training sets which are too small, affects the performance of a classifier. Recognizing the importance of second-order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high-dimensional data on the other, we propose a method to aid visualization of high-dimensional statistics using a color coding scheme.

Lee, Chulhee↗
NASA NTRS1990

Use of high-dimensional spectral data to evaluate organic matter, reflectance relationships in soils

Recent breakthroughs in remote sensing technology have led to the development of a spaceborne high spectral resolution imaging sensor, HIRIS, to be launched in the mid-1990s for observation of earth surface features. The effects of organic carbon content on soil reflectance over the spectral range of HIRIS, and to examine the contributions of humic and fulvic acid fractions to soil reflectance was evaluated. Organic matter from four Indiana agricultural soils was extracted, fractionated, and purified, and six individual components of each soil were isolated and prepared for spectral analysis. The four soils, ranging in organic carbon content from 0.99 percent, represented various combinations of genetic parameters such as parent material, age, drainage, and native vegetation. An experimental procedure was developed to measure reflectance of very small soil and organic component samples in the laboratory, simulating the spectral coverage and resolution of the HIRIS sensor. Reflectance in 210 narrow (10 nm) bands was measured using the CARY 17D spectrophotometer over the 400 to 2500 nm wavelength range. Reflectance data were analyzed statistically to determine the regions of the reflective spectrum which provided useful information about soil organic matter content and composition. Wavebands providing significant information about soil organic carbon content were located in all three major regions of the reflective spectrum: visible, near infrared, and middle infrared. The purified humic acid fractions of the four soils were separable in six bands in the 1600 to 2400 nm range, suggesting that longwave middle infrared reflectance may be useful as a non-destructive laboratory technique for humic acid characterization.

Henderson, T. L.↗
NASA NTRS1998

Demonstrations at Supercomputing 1998

Contents of the papers include: Visualization techniques- The next generation, Small-machine visualizing very big data sets, Public display of Lunar Prospector data, Steering molecules via Feel, High-dimensional data browser, Multi-source visualization, Interactive surface flow visualization, and the Virtual Windtunnel.

Bryson, Steve↗
NASA NTRS1994

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Hoffbeck, Joseph P.↗
NASA NTRS1998

Support Vector Machines for Hyperspectral Remote Sensing Classification

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Gualtieri, J. Anthony↗
NASA NTRS1992

Spectral analysis for automated exploration and sample acquisition

Future space exploration missions will rely heavily on the use of complex instrument data for determining the geologic, chemical, and elemental character of planetary surfaces. One important instrument is the imaging spectrometer, which collects complete images in multiple discrete wavelengths in the visible and infrared regions of the spectrum. Extensive computational effort is required to extract information from such high-dimensional data. A hierarchical classification scheme allows multispectral data to be analyzed for purposes of mineral classification while limiting the overall computational requirements. The hierarchical classifier exploits the tunability of a new type of imaging spectrometer which is based on an acousto-optic tunable filter. This spectrometer collects a complete image in each wavelength passband without spatial scanning. It may be programmed to scan through a range of wavelengths or to collect only specific bands for data analysis. Spectral classification activities employ artificial neural networks, trained to recognize a number of mineral classes. Analysis of the trained networks has proven useful in determining which subsets of spectral bands should be employed at each step of the hierarchical classifier. The network classifiers are capable of recognizing all mineral types which were included in the training set. In addition, the major components of many mineral mixtures can also be recognized. This capability may prove useful for a system designed to evaluate data in a strange environment where details of the mineral composition are not known in advance.

Eberlein, Susan↗
NASA NTRS1992

Search for subgrid scale parameterization by projection pursuit regression

The dependence of subgrid-scale stresses on variables of the resolved field is studied using direct numerical simulations of isotropic turbulence, homogeneous shear flow, and channel flow. The projection pursuit algorithm, a promising new regression tool for high-dimensional data, is used to systematically search through a large collection of resolved variables, such as components of the strain rate, vorticity, velocity gradients at neighboring grid points, etc. For the case of isotropic turbulence, the search algorithm recovers the linear dependence on the rate of strain (which is necessary to transfer energy to subgrid scales) but is unable to determine any other more complex relationship. For shear flows, however, new systematic relations beyond eddy viscosity are found. For the homogeneous shear flow, the results suggest that products of the mean rotation rate tensor with both the fluctuating strain rate and fluctuating rotation rate tensors are important quantities in parameterizing the subgrid-scale stresses. A model incorporating these terms is proposed. When evaluated with direct numerical simulation data, this model significantly increases the correlation between the modeled and exact stresses, as compared with the Smagorinsky model. In the case of channel flow, the stresses are found to correlate with products of the fluctuating strain and rotation rate tensors. The mean rates of rotation or strain do not appear to be important in this case, and the model determined for homogeneous shear flow does not perform well when tested with channel flow data. Many questions remain about the physical mechanisms underlying these findings, about possible Reynolds number dependence, and, given the low level of correlations, about their impact on modeling. Nevertheless, demonstration of the existence of causal relations between sgs stresses and large-scale characteristics of turbulent shear flows, in addition to those necessary for energy transfer, provides important insight into the relation between scales in turbulent flows.

Meneveau, C.↗
NASA NTRS1989

A spectral feature design system for the HIRIS/MODIS era

A spectral feature design system for high-dimensional multispectral data is described. This system utilizes hard-limited or infinitely clipped optimal transforms and canonical analysis to extract the spectral features for data volume reduction and classification purposes. The design procedure is intended to be application-specific in order to make it maximally effective for each use. Although it could be used in a variety of circumstances, the procedure was designed with satellite data collection in mind, such as will be needed with the High-Resolution Imaging Spectrometer (HIRIS), a land-oriented Earth observational sensor intended for launch in the mid-1990s. The computation required for the steps prior to the actual satellite data collection are straightforward and could be done with readily available subroutines. The procedure is also defined in such a way as to require only very simple onboard calculations. The tests reported provide substantial data volume reduction in the satellite-to-Earth downlink and subsequent computation phases, while maintaining satisfactory classification accuracy.

Chen, Chih-Chien Thomas↗
NASA NTRS1987

An unsupervised feature extraction method for high dimensional image data compaction

A new on-line unsupervised feature extraction method for high-dimensional remotely sensed image data compaction is presented. This method can be utilized to solve the problem of data redundancy in scene representation by satellite-borne high resolution multispectral sensors. The algorithm first partitions the observation space into an exhaustive set of disjoint objects. Then, pixels that belong to an object are characterized by an object feature. Finally, the set of object features is used for data transmission and classification. The example results show that the performance with the compacted features provides a slight improvement in classification accuracy instead of any degradation. Also, the information extraction method does not need to be preceded by a data decompaction.

Ghassemian, Hassan↗
NASA NTRS1990

Visions of visualization aids: Design philosophy and experimental results

Aids for the visualization of high-dimensional scientific or other data must be designed. Simply casting multidimensional data into a two- or three-dimensional spatial metaphor does not guarantee that the presentation will provide insight or parsimonious description of the phenomena underlying the data. Indeed, the communication of the essential meaning of some multidimensional data may be obscured by presentation in a spatially distributed format. Useful visualization is generally based on pre-existing theoretical beliefs concerning the underlying phenomena which guide selection and formatting of the plotted variables. Two examples from chaotic dynamics are used to illustrate how a visulaization may be an aid to insight. Two examples of displays to aid spatial maneuvering are described. The first, a perspective format for a commercial air traffic display, illustrates how geometric distortion may be introduced to insure that an operator can understand a depicted three-dimensional situation. The second, a display for planning small spacecraft maneuvers, illustrates how the complex counterintuitive character of orbital maneuvering may be made more tractable by removing higher-order nonlinear control dynamics, and allowing independent satisfaction of velocity and plume impingement constraints on orbital changes.

Ellis, Stephen R.↗
NASA NTRS1989

Visions of visualization aids - Design philosophy and observations

Aids for the visualization of high-dimensional scientific or other data must be designed. Simply casting multidimensional data into a two-dimensional or three-dimensional spatial metaphor does not guarantee that the presentation will provide insight or a parsimonious description of phenomena implicit in the data. Useful visualization, in contrast to glitzy, high-tech, computer-graphics imagery, is generally based on preexisting theoretical beliefs concerning the underlying phenomena. These beliefs guide selection and formatting of the plotted variables. Visualization tools are useful for understanding naturally three-dimensional data bases such as those used by pilots or astronauts. Two examples of such aids for spatial maneuvering illustrate that informative geometric distortion may be introduced to assist visualization and that visualization of complex dynamics alone may not be adequate to provide the necessary insight into the underlying processes.

Ellis, Stephen R.↗
NASA NTRS1990

Topics in inference and decision-making with partial knowledge

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data.

Safavian, S. Rasoul↗
NASA NTRS1988

Image processing software for imaging spectrometry

The paper presents a software system, Spectral Analysis Manager (SPAM), which has been specifically designed and implemented to provide the exploratory analysis tools necessary for imaging spectrometer data, using only modest computational resources. The basic design objectives are described as well as the major algorithms designed or adapted for high-dimensional images. Included in a discussion of system implementation are interactive data display, statistical analysis, image segmentation and spectral matching, and mixture analysis.

Mazer, Alan S.↗
NASA NTRS1991

Indications of low dimensionality in magnetospheric dynamics

Using three separate but related approaches, the question of whether the dynamic response of the magnetosphere to the solar wind input may be described by a low-order system of equations is examined. First, it is determined that the dimension of the subset (the attractor) in the high-dimensional magnetospheric phase space associated with the westward auroral electrojet (AL) index for some of the data sets compiled by Bargatze et al. (1985) is 4.0 + or - 0.2, seemingly independent of activity level. Second, direct modeling of the magnetosphere, considering the bulk properties of the tail plasma, leads to a system of equations that is similar to those previously reported as a dripping faucet model; here, the focus is specifically on the prediction of a natural frequency in this model. Finally, a peak is identified with the predicted frequency in power spectra of AL computed for intervals with both low and high activity. Peaks at other frequencies also appear in the spectra, and such resonances would be expected for a chaotic nonlinear oscillator. Combining these approaches it is concluded that at least some aspects of magnetospheric dynamics may be meaningfully modeled by low-dimensional sets of equations.

Roberts, D. A.↗
NASA NTRS1996

Communications and information research: Improved space link performance via concatenated forward error correction coding

With the development of new advanced instruments for remote sensing applications, sensor data will be generated at a rate that not only requires increased onboard processing and storage capability, but imposes demands on the space to ground communication link and ground data management-communication system. Data compression and error control codes provide viable means to alleviate these demands. Two types of data compression have been studied by many researchers in the area of information theory: a lossless technique that guarantees full reconstruction of the data, and a lossy technique which generally gives higher data compaction ratio but incurs some distortion in the reconstructed data. To satisfy the many science disciplines which NASA supports, lossless data compression becomes a primary focus for the technology development. While transmitting the data obtained by any lossless data compression, it is very important to use some error-control code. For a long time, convolutional codes have been widely used in satellite telecommunications. To more efficiently transform the data obtained by the Rice algorithm, it is required to meet the a posteriori probability (APP) for each decoded bit. A relevant algorithm for this purpose has been proposed which minimizes the bit error probability in the decoding linear block and convolutional codes and meets the APP for each decoded bit. However, recent results on iterative decoding of 'Turbo codes', turn conventional wisdom on its head and suggest fundamentally new techniques. During the past several months of this research, the following approaches have been developed: (1) a new lossless data compression algorithm, which is much better than the extended Rice algorithm for various types of sensor data, (2) a new approach to determine the generalized Hamming weights of the algebraic-geometric codes defined by a large class of curves in high-dimensional spaces, (3) some efficient improved geometric Goppa codes for disk memory systems and high-speed mass memory systems, and (4) a tree based approach for data compression using dynamic programming.

Rao, T. R. N.↗
NASA NTRS1995

How far away is plug 'n' play? Assessing the near-term potential of sonification and auditory display

The commercial music industry offers a broad range of plug 'n' play hardware and software scaled to music professionals and scaled to a broad consumer market. The principles of sound synthesis utilized in these products are relevant to application in virtual environments (VE). However, the closed architectures used in commercial music synthesizers are prohibitive to low-level control during real-time rendering, and the algorithms and sounds themselves are not standardized from product to product. To bring sound into VE requires a new generation of open architectures designed for human-controlled performance from interfaces embedded in immersive environments. This presentation addresses the state of the sonic arts in scientific computing and VE, analyzes research challenges facing sound computation, and offers suggestions regarding tools we might expect to become available during the next few years. A list of classes of audio functionality in VE includes sonification -- the use of sound to represent data from numerical models; 3D auditory display (spatialization and localization, also called externalization); navigation cues for positional orientation and for finding items or regions inside large spaces; voice recognition for controlling the computer; external communications between users in different spaces; and feedback to the user concerning his own actions or the state of the application interface. To effectively convey this considerable variety of signals, we apply principles of acoustic design to ensure the messages are neither confusing nor competing. We approach the design of auditory experience through a comprehensive structure for messages, and message interplay we refer to as an Automated Sound Environment. Our research addresses real-time sound synthesis, real-time signal processing and localization, interactive control of high-dimensional systems, and synchronization of sound and graphics.

Bargar, Robin↗
NASA NTRS1991

Hierarchical classifier design in high-dimensional, numerous class cases

As progress in new sensor technology continues, increasingly high spectral resolution sensors are being developed. These sensors give more detailed and complex data for each picture element and greatly increase the dimensionality of data over past systems. Three methods for designing a decision tree classifier are discussed; a top down approach, a bottom up approach, and a hybrid approach. Three feature extraction techniques are implemented. Canonical and extended canonical techniques are mainly dependent on the mean difference between two classes. An autocorrelation technique is dependent on the correlation differences. The mathematical relationship among sample size, dimensionality, and risk value is derived.

Kim, Byungyong↗