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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Self-organizing map (SOM) of space acceleration measurement system (SAMS) data

In this paper, space acceleration measurement system (SAMS) data have been classified using self-organizing map (SOM) networks without any supervision; i.e., no a priori knowledge is assumed regarding input patterns belonging to a certain class. Input patterns are created on the basis of power spectral densities of SAMS data. Results for SAMS data from STS-50 and STS-57 missions are presented. Following issues are discussed in details: impact of number of neurons, global ordering of SOM weight vectors, effectiveness of a SOM in data classification, and effects of shifting time windows in the generation of input patterns. The concept of 'cascade of SOM networks' is also developed and tested. It has been found that a SOM network can successfully classify SAMS data obtained during STS-50 and STS-57 missions.

STS-57 Shuttle Project↗
NASA NTRS1972

A geometrical interpretation of the 2n-th central difference

Many algorithms used for data smoothing, data classification and error detection require the calculation of the distance from a point to the polynomial interpolating its 2n neighbors (n on each side). This computation, if performed naively, would require the solution of a system of equations and could create numerical problems. This note shows that if the data is equally spaced, then this calculation can be performed using a simple recursion formula.

Tapia, R. A.↗
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
NASA NTRS1993

Satellite inventory of Minnesota forest resources

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Bauer, Marvin E.↗
NASA NTRS2009

Hybrid NN/SVM Computational System for Optimizing Designs

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan↗
NASA NTRS1991

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗
NASA NTRS1990

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data sources. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗
NASA NTRS2021

Petrology, Geochemistry, and Pairing of Lunar Meteorites from the Dominion Range

Introduction: During the 2018-2019 Antarctic Search for Meteorites (ANSMET) field season in the Dominion Range (DOM), 7 lunar meteorite stones were collected: DOM 18242 (15.1 g), DOM 18244 (25.1 g), DOM 18262 (6.8 g), DOM 18509 (16.5 g), DOM 18543 (13.6 g), DOM 18666 (45.9 g), and DOM 18678 (11.6 g). Here we present the initial results of electron microprobe and X-ray computed tomography (XCT) studies of these stones and look at the details of their petrography and mineral chemistry, as well as investigate possible pairing relationships, both with each other and with previously described lunar meteorites. Most of the work presented here is on the DOM 18509, 18543, and 18678 stones; subsamples of the other stones are in hand and similar measurements will be made on them by the time of the meeting. Methods: Textures in DOM 18509, DOM 18543, and DOM 18678 were characterized in 2D by optical microscopy, backscattered electron (BSE) imaging and elemental X-ray images on thin sections, as well as in 3D by X-ray computed tomography (XCT) on sample chips. Mineral compositions were assessed through a combination of wavelength dispersive spectroscopy EPMA (electron probe microanalysis) and x-ray mapping on the JEOL 8530 at NASA JSC. The bulk composition of all three meteorites was determined based on analyses of the fusion crust glass. XCT analyses were done on the Nikon XTH 320 at NASA JSC. ICP-MS data on bulk rock subsamples for each meteorite will be carried out in the near future. Results: The stones are all similar in macroscopic appearance with a dark aphanitic matrix hosting a variety of small- to medium-sized angular mineral and lithic fragments (often light colored in nature) [1,2]. Based on EPMA and XCT results, the three meteorites are polymict regolith breccias comprised of mineral, glass, and lithic clasts ranging up to several mm in length. Melt veins run through all three meteorite samples. Mineral clasts in all 3 stones are dominated by pyroxene and plagioclase (An82-96), with minor amounts of SiO2, olivine (Fo1-52), and FeTi-oxides. Pyroxene grains are mostly Fe-rich pigeonite and augite, and larger clasts are normally zoned and have fine exsolution lamellae. The lithic clasts in all stones consists of: (1) basalt clasts that contain zoned pyroxene, plagioclase laths, and ilmenite, with minor silica and Fe-rich olivine; (2) granulitic clasts; (3) anorthosite clasts; (4) Si-rich clasts that also contain ilmenite, troilite, high-Ca pyroxene, fayalite, and K-feldspar likely mesostasis from late stage basalts). All three meteorites contain glassy fusion crust that is highly vesicular, high in FeO and Al2O3 (15-17 wt% each), ferroan (Mg# of 23-24), and moderately rich in TiO2 (1.4-1.8 wt%). The composition of the fusion crust can serve as a proxy for the bulk meteorite composition and is identical within error for all three meteorites. Implications: The lithic and mineral clasts in all three stones are similar in clast population and assemblages as well as mineral chemistry. In addition, the fusion crust composition, a proxy for bulk composition, is within error of each other for all three stones. Thus DOM 18509, DOM 18543, and DOM 18678 are almost certainly paired. Based on similarities in macroscopic description as well as preliminary classification data [1,2], all 7 lunar stones from DOM are likely paired, though additional quantitative analyses are needed to confirm this. The presence of spherules and vesicular fusion crusts indicates that the DOM pairing group is a regolith breccia. The presence of basaltic and gabbroic clasts as well as more feldspathic materials, suggest that the regolith from which these meteorites formed contained a mixture of feldspathic highland material and mare material, suggesting a possible provenance near a marehighlands boundary. No evidence of KREEPy lithologies have been observed so far in these meteorites, however, future ICP-MS data on bulk rock chips for the stones will reveal any KREEP component if present. The DOM pairing group has many similarities to previously described lunar breccia meteorite MET 01210, however more detailed compositional data will be needed to make a definitive comparison.

R.A. Zeigler↗
NASA NTRS1984

Using spatial logic in classification of Landsat TM data

A strategy for spatial/spectral classification of Landsat TM data is presented. The strategy is founded upon 'spatial logic', a logic that seeks to emulate important aspects of visual image interpretation. The carefully structured classification process begins with spectral stratification of the data into water, vegetated and non-vegetated pixels. A region growing algorithm is then used to define 'fields' of similar land cover composition. Fields are characterized by cover composition, size and neighborhood characteristics. A supervised iterative contextual classification algorithm is developed to assign final land use/land cover labels. Maps are generalized using a spatial post-processing technique. Positive, though preliminary, results are presented.

Merchant, J. W.↗
NASA NTRS1973

The use of the temporal dimension in classifying and mapping ERTS-1 MSS data

Multispectral data from two ERTS-1 scenes of the same central Pennsylvania area were brought into registration by translation and then merged. The two scenes were viewed on different dates, but from adjacent ground tracks, as frequent cloud cover in Pennsylvania made it impossible to choose two scenes from the same track. Targets selected to be mapped included river water, railroad yards, creeks, urban areas, industrial areas, and vegetation. Equivalent training areas were chosen from each of the original scenes and from the merged data. Classification maps were produced for each, and a comparison was made. Scene brightness was found to have the most important effect on classification differences.

Borden, F. Y.↗
NASA NTRS1976

Applications of feature selection

The use of satellite-acquired (LANDSAT) multispectral scanner (MSS) data to conduct an inventory of some crop of economic interest such as wheat over a large geographical area is considered in relation to the development of accurate and efficient algorithms for data classification. The dimension of the measurement space and the computational load for a classification algorithm is increased by the use of multitemporal measurements. Feature selection/combination techniques used to reduce the dimensionality of the problem are described.

Guseman, L. F., Jr.↗
NASA NTRS1980

International Symposium on Remote Sensing of Environment, 14th, San Jose, Costa Rica, April 23-30, 1980, Proceedings. Volumes 1, 2 & 3

Papers are presented on remote sensing applications in resource monitoring and management, data classification and modeling procedures, and the use of remote sensing techniques in developing nations. The subjects of land use/land cover, soil mapping, crop identification, mapping of geological resources, renewable resource analysis, and oceanographic applications are discussed. Papers from Argentina, Bolivia, Brazil, Costa Rica, the Syrian Arab Republic, the People's Republic of China, the Phillipines, Italy, Upper Volta and the United States are included.

Source record↗
NASA NTRS1985

KARL: A Knowledge-Assisted Retrieval Language

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Dominick, Wayne D.↗
NASA NTRS1992

Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. The proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photorefractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feed forward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

Liu, Hua-Kuang↗