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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Implementation of an advanced table look-up classifier for large area land-use classification

Software employing Eppler's improved table look-up approach to pattern recognition has been developed, and results from this software are presented. The look-up table for each class is a computer representation of a hyperellipsoid in four dimensional space. During implementation of the software Eppler's look-up procedure was modified to include multiple ranges in order to accommodate hollow regions in the ellipsoids. In a typical ERTS classification run less than 6000 36-bit computer words were required to store tables for 24 classes. Classification results from the improved table look-up are identical with those produced by the conventional method, i.e., by calculation of the maximum likelihood decision rule at the moment of classification. With the new look-up approach an entire ERTS MSS frame can be classified into 24 classes in 1.3 hours, compared to 22.5 hours required by the conventional method. The new software is coded completely in FORTRAN to facilitate transfer to other digital computers.

Jones, C.↗
NASA NTRS2024

Bulk Major and Trace Elemental Composition of an Aggregate Sample From Asteroid Bennu

On September 24, 2023, NASA’s OSIRIS-REx mission returned a capsule to Earth carrying material from asteroid Bennu. This event was the first time a U.S. mission delivered pristine samples from an asteroid and is the largest asteroid sample return to date. As these samples represent some of the oldest, most primitive, and pristine materials available to us, and which originate from a known and well-studied asteroid, they allow us a rare opportunity to gain a better understanding of the formation and evolution of our solar system. Key to understanding the material returned from asteroid Bennu is establishing its bulk chemical composition. Previous studies have shown that each chondrite group has a distinct elemental composition. For the carbonaceous chondrites specifically, each group exhibits a distinct pattern of moderately and highly volatile elemental depletions, relative to CI chondrites. CI chondrites are considered the most primitive chondrite group and broadly represent the solar photosphere composition. The two most striking features of these depletion patterns is that the moderately volatile element depletions increase with decreasing 50% condensation temperature and then plateau out at abundances that roughly correlate with matrix abundance. Due to these distinctive patterns, bulk elemental composition has become an important classification tool for establishing the different chondrite groups and the connections between them. As such, determining the bulk chemical composition of the Bennu aggregates will help to test two mission hypothesis: “Bennu’s bulk elemental composition reflects that of its main parent asteroid and is similar to the composition of the Sun, with depletions in moderately to highly volatile elements” and “Bennu’s dominant lithologies are comparable in bulk mineralogy, petrology, and composition to the most aqueously altered carbonaceous chondrites” . Furthermore as the carbonaceous and non-carbonaceous chondrites are thought to have likely formed in the inner and outer protoplanetary disk, respectively (e.g., [8]), determining the bulk chemical compositions of the Bennu aggregates will also help test the major mission hypothesis that “Bennu's parent body formed beyond the snow line by accretion of material in the pro-toplanetary disk” . Regarding chondrite formation, observed elemental patterns have been successfully used to model how the mixing of volatile-rich and volatile-poor chondritic components can produce the observed carbonaceous chondrites groups. They have also been significant in investigating how volatilization processes influenced chondrite formation. As such, establishing bulk chemical compositions of the pristine Bennu samples is vital for understanding the asteroid, and solar system formation. To begin this processes, we analyzed the bulk major and trace elemental compositions of Bennu aggregates.

P Koefoed↗
NASA NTRS1992

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Newton, Scott C.↗
NASA NTRS1973

Remote sensing techniques for support of coastal zone resource management.

Description of remote sensing studies carried out for the purpose of developing and/or demonstrating techniques which can be employed for land use inventory, marsh vegetation classification, and water characteristics surveys. Attention is given to results obtained with (1) photo interpretation techniques and procedures for the development of land use information from high-altitude aircraft and satellite imagery, (2) computer based pattern recognition techniques utilizing multispectral scanner data for marsh vegetation classification, and (3) infrared and microwave techniques for the monitoring and surveying of coastal water temperature and salinity characteristics.

Piland, R. O.↗
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 NTRS1978

Bayesian classification in a time-varying environment

The problem of classifying a pattern based on multiple observation made in a time-varying environment is analyzed. The identity of the pattern may itself change. A Bayesian solution is derived, after which the conditions of the physical situation are invoked to produce a cascade classifier model. Experimental results based on remote sensing data demonstrate the effectiveness of the classifier.

Swain, P. H.↗
NASA NTRS1976

Investigation of environmental change pattern in Japan

The author has identified the following significant results. A detailed land use classification for a large urban area of Tokyo was made using MSS digital data. It was found that residential, commercial, industrial, and wooded areas and grasslands can be successfully classified. A mesoscale vortex associated with large ocean current, Kuroshio, which is a rare phenomenon, was recognized visually through the analysis of MSS data. It was found that this vortex affects the effluent patterns of rivers. Lava flowing from Sakurajima Volcano was clearly classified for three major erruptions (1779, 1914, and 1946) using MSS data.

Maruyasu, T.↗
NASA NTRS1975

A study to analyze six band multispectral images and fabricate a Fourier transform detector

An automatic Fourier transform diffraction pattern sampling system, used to investigate techniques for forestry classification of six band multispectral aerial photography is presented. Photographs and diagrams of the design, development and fabrication of a hybrid optical-digital Fourier transform detector are shown. The detector was designed around a concentric ring fiber optic array. This array was formed from many optical fibers which were sorted into concentric rings about a single fiber. All the fibers in each ring were collected into a bundle and terminated into a single photodetector. An optical/digital interface unit consisting of a high level multiplexer, and an analog-to-digital amplifier was also constructed and is described.

Shackelford, R. G.↗
NASA NTRS1977

A technique for the determination of Louisiana marsh salinity zone from vegetation mapped by multispectral scanner data: A comparison of satellite and aircraft data

Vegetation in selected study areas on the Louisiana coast was mapped using low altitude aircraft and satellite (LANDSAT) multispectral scanner data. Fresh, brackish, and saline marshes were then determined from the remotely sensed presence of dominant indicator plant associations. Such vegetational classifications were achieved from data processed through a standard pattern recognition computer program. The marsh salinity zone maps from the aircraft and satellite data compared favorably within the broad salinity regimes. The salinity zone boundaries determined by remote sensing compared favorably with those interpolated from line-transect field observations from an earlier year.

Butera, M. K.↗
NASA NTRS1990

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Smith, William L.↗
NASA NTRS1993

Local topography of Mars and its relationship to surface weathering processes

There is a growing body of evidence in favor of the importance of aqueous sedimentary processes on Mars. It is important to understand the role that surface weathering processes have played in the development of the present morphology of the Martian surface. Such an understanding is important not only for its relevance to the study of volatile sources and sinks on Mars through time, but also for its relevance to Martian geologic and tectonic history. Starting in the fall of this year, the Mars Observer Laser Altimeter will begin sending back to Earth data on the topography of Mars that is of a higher quality than most of the topography data available for the Earth. This data will be invaluable, not only for understanding global and large-scale regional processes and landforms on Mars, but also for the study of local and smaller-scale regional processes and landforms. Digital topography is an important part of geologic and geomorphic studies, useful in distinguishing between different lithologies and between different types of weathering. Digital topography data may be used to study a wide variety of local and regional-scale landforms, including valleys, sand dunes, lava flows, landslides, and slopes. Topography data are also essential to the analysis of spectral response patterns, especially in areas of high topographic relief. Geomorphic classification can be significantly improved by the addition of topographic information.

Schaefer, M. W.↗
NASA NTRS1973

A study of morphology, provenance, and movement of desert sand seas in Africa, Asia, and Australia

The author has identified the following significant results. The assembling of photomosaics from color prints of ERTS images has made possible the recognition and description of san patterns, and these are to be the basis of a worldwide classification of sand bodies. Progress has been as rapid as the acquisition of the bulk composite images will permit and for some areas the mosaics are now nearly complete. A second step, which consists of the accumulation and analysis of regional wind data, to be plotted as overlays for the dune patterns, is now underway and should soon give tangible results. Other aspects of the project include the gathering of ground truth in the form of air photographs, needed to interpret geomorphic forms and the sampling of sand deposits for analyzing the texture and composition of sand bodies. A start has been made on these studies in several selected areas.

Mckee, E. D.↗
NASA NTRS1993

Quantitative EEG patterns of differential in-flight workload

Four test pilots were instrumented for in-flight EEG recordings using a custom portable recording system. Each flew six, two minute tracking tasks in the Calspan NT-33 experimental trainer at Edwards AFB. With the canopy blacked out, pilots used a HUD display to chase a simulated aircraft through a random flight course. Three configurations of flight controls altered the flight characteristics to achieve low, moderate, and high workload, as determined by normative Cooper-Harper ratings. The test protocol was administered by a command pilot in the back seat. Corresponding EEG and tracking data were compared off-line. Tracking performance was measured as deviation from the target aircraft and combined with control difficulty to achieve an estimate of 'cognitive workload'. Trended patterns of parietal EEG activity at 8-12 Hz were sorted according to this classification. In all cases, high workload produced a significantly greater suppression of 8-12 Hz activity than low workload. Further, a clear differentiation of EEG trend patterns was obtained in 80 percent of the cases. High workload produced a sustained suppression of 8-12 Hz activity, while moderate workload resulted in an initial suppression followed by a gradual increment. Low workload was associated with a modulated pattern lacking any periods of marked or sustained suppression. These findings suggest that quantitative analysis of appropriate EEG measures may provide an objective and reliable in-flight index of cognitive effort that could facilitate workload assessment.

Sterman, M. B.↗
NASA NTRS1993

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

Paola, Justin D.↗
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 comparative study of nonparametric methods for pattern recognition

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal.

Hahn, S. F.↗
NASA NTRS1973

Terrain classification maps of Yellowstone National Park

A cooperative ERTS-1 investigation involving U. S. Geological Survey, National Park Service, and Environmental Research Institure of Michigan (ERIM) personnel has as its goal the preparation of terrain classification maps for the entire Yellowstone National Park. Excellent coverage of the park was obtained on 6 August 1972 (frame 1015-17404). Preliminary terrain classification maps have been prepared at ERIM by applying multispectral pattern recognition techniques to ERTS-MSS digital taped data. The color coded terrain maps are presented and discussed. The discussion includes qualitative and quantitative accuracy estimates and discussion of processing techniques.

Thomson, F. J.↗