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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Craters on Earth, Moon, and Mars - Multivariate classification and mode of origin

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian calderas, and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components.

Pike, R. J.↗
NASA NTRS1975

Classification of wetlands vegetation using small scale color infrared imagery

A classification system for Chesapeake Bay wetlands was derived from the correlation of film density classes and actual vegetation classes. The data processing programs used were developed by the Laboratory for the Applications of Remote Sensing. These programs were tested for their value in classifying natural vegetation, using digitized data from small scale aerial photography. Existing imagery and the vegetation map of Farm Creek Marsh were used to determine the optimal number of classes, and to aid in determining if the computer maps were a believable product.

Williamson, F. S. L.↗
NASA NTRS1974

Study of USGS/NASA land use classification system

It is known from several previous investigations that many categories of land-use can be mapped via computer processing of Earth Resources Technology Satellite data. The results are presented of one such experiment using the USGS/NASA land-use classification system. Douglas County, Georgia, was chosen as the test site for this project. It was chosen primarily because of its recent rapid growth and future growth potential. Results of the investigation indicate an overall land-use mapping accuracy of 67% with higher accuracies in rural areas and lower accuracies in urban areas. It is estimated, however, that 95% of the State of Georgia could be mapped by these techniques with an accuracy of 80% to 90%.

Spann, G. W.↗
NASA NTRS1975

Classification of physiography from ERTS imagery

The potential application of optical data processing to ERTS imagery as a means for automatic identification of large-scale ground patterns was investigated. Spatial frequency distribution and orientational information were derived from ERTS-1 imagery of Kansas for each of 80 sample areas, each 37 km in diameter. The application of classification algorithms to this data reveals that a high degree of correlation exists between the physiography of a sample area and its frequency information. Specifically, the band of frequencies between 1.1 and 2.8 cycles/km appear to contain most of the information needed in distinguishing different physiographic regions.

Ulaby, F. T.↗
NASA NTRS1975

Classification and properties of iron meteorites

The paper describes the general requirements for a genetically significant scheme for classifying iron meteorites. Some of the properties which may be used to classify iron meteorites are reviewed, and a classification scheme based on Ga-Ni and Ge-Ni plots, taxonomic properties, and chemical properties is proposed. It is found that 95% of the irons can be assigned to a genetic group or an anomalous class.

Scott, E. R. D.↗
NASA NTRS1975

An examination of the potential applications of automatic classification techniques to Georgia management problems

Automatic classification techniques are described in relation to future information and natural resource planning systems with emphasis on application to Georgia resource management problems. The concept, design, and purpose of Georgia's statewide Resource AS Assessment Program is reviewed along with participation in a workshop at the Earth Resources Laboratory. Potential areas of application discussed include: agriculture, forestry, water resources, environmental planning, and geology.

Rado, B. Q.↗
NASA NTRS1975

Land use classification in Bolivia

The Bolivian LANDSAT Program is an integrated, multidisciplinary project designed to provide thematic analysis of LANDSAT, Skylab, and other remotely sensed data for natural resource management and development in Bolivia, is discussed. Among the first requirements in the program is the development of a legend, and appropriate methodologies, for the analysis and classification of present land use based on landscape cover. The land use legend for Bolivia consists of approximately 80 categories in a hierarchical organization which may be collapsed for generalization, or expanded for greater detail. The categories, and their definitions, provide for both a graphic and textual description of the complex and diverse landscapes found in Bolivia, and are designed for analysis from LANDSAT and other remotely sensed data at scales of 1:1,000,000 and 1:250,000. Procedures and example products developed are described and illustrated, for the systematic analysis and mapping of present land use for all of Bolivia.

Brockmann, C. E.↗
NASA NTRS1976

Texture measurements for the automatic classification of imagery

The stated purpose is to demonstrate the applicability of texture measurements for making distinctions between classes of imagery. Multispectral images obtained from aircraft and satellites have been successfully delineated into land use classes on the basis of density in the different spectral bands. However, spatial patterns can add additional information to improve classification accuracy. A comparison is made between the results obtained using five texture algorithms for separating land use classes using ERTS imagery. The transforms evaluated are the Karhunen-Loeve, the fast Fourier, the Walsh-Hadamard, the Slant, and a digital matched filter.

Kirvida, L.↗
NASA NTRS1975

Classification of terrain models for Martian vehicle

This paper develops a procedure for an autonomous Martian rover to classify objects into obstacles and non-obstacles. These so called 'objects' are actually models generated by the Terrain Modeling and Edge Detection schemes. The classification process is performed using a set of object parameters which distinguish unobstructive objects from obstacles.

Leung, K. L.↗
NASA NTRS1977

Classification accuracy improvement

Improvements made in processing system designed for MIDAS (prototype multivariate interactive digital analysis system) effects higher accuracy in classification of pixels, resulting in significantly-reduced processing time. Improved system realizes cost reduction factor of 20 or more.

Kistler, R.↗
NASA NTRS1972

Apollo 15 coarse fines (4-10 mm): Sample classification, description and inventory

A particle by particle binocular microscopic examination of all of the Apollo 15 4-10 mm fines samples is reported. These particles are classified according to their macroscopic lithologic features in order to provide a basis for sample allocations and future study. The relatively large size of these particles renders them too vaulable to permit treatment along with the other bulk fines, yet they are too small (and numerous) to practically receive full individual descriptive treatment as given the larger rock samples. This examination, classification and description of subgroups represents a compromise treatment. In most cases and for many types of investigation the individual particles should be large enough to permit the application of more than one type of analysis.

Powell, B. N.↗
NASA NTRS1976

Linear dimensionality of Landsat agricultural data with implications for classification

A model for the Landsat multispectral scanner data, representing a generalization of the commonly used Gaussian model, has been formulated and analyzed. The model hypothesizes that the data for different crop types essentially lie on distinct hyperplanes in the feature space. Tests of this model reveal that: (1) the agricultural data from any single acquisition (i.e., four-channel) of Landsat are essentially two dimensional, regardless of the crop type; and (2) the data from different sites and different stages of crop development all lie on planes which are parallel. These findings have significant implications for data display, classification, feature extraction, and signature extension.

Wheeler, S. G.↗
NASA NTRS1976

Selecting class weights to minimize classification bias in acreage estimation

Preliminary results of experiments being performed to select optimal class weights for use with the maximum likelihood classifier in acreage estimation using remote sensor imagery are presented. These weights will be optimal in the sense that the bias will be minimized in the proportion estimate obtained from the classification results by sample counting. The procedure was tested using Landsat MSS data from an 8 by 9.6 km area of ground truth in Finney County, Kansas.

Belcher, W. M.↗
NASA NTRS1976

Number of signatures necessary for accurate classification

This paper presents a procedure for determining the number of signatures to use in classifying multispectral scanner data. A large initial set of signatures is obtained by clustering the training points within each category (such as 'wheat' or 'other') to be recognized. These clusters are then combined into broader signatures by a program that considers each pair of signatures within a category, combines the best pair in the light of certain criteria, saves the combined signature and repeats the procedure until there is one signature for each category. The result is a collection of sets of signatures, one set for each number between the number of initial clusters and the number of categories. With the aid of statistics such as an estimate of the probability of misclassification between categories, the user can choose the smallest set satisfying his requirements for classification accuracy.

Richardson, W.↗
NASA NTRS1976

Evaluation of classification procedures for estimating wheat acreage in Kansas

This report presents the results of experiments which were performed to evaluate procedures for estimating wheat acreage in intensive test sites (ITS's) in Kansas. An analyst/interpreter (AI) selected and labeled fields from Landsat-1 satellite imagery. Statistics were generated for each selected ITS, and the imagery was classified using a maximum likelihood classifier. Various components of the classification process were tested.

Flores, L. M.↗