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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 NTRS1985

The effects of sensor advancements on Thematic Mapper data classification

Analyses of Landsat Thematic Mapper (TM) data were conducted to assess the effects of sensor advancements on the thematic classification of remote sensing data. The effects of altering three sensor characteristics (spatial resolution, data quantization, and spectral band configuration) from Landsat Multispectral Scanner (MSS) specifications were investigated using analysis-of-variance (ANOVA). Analyses were conducted on data from two TM scenes: Washington, D.C. (late autumn) and western Pennsylvania (late summer). Results indicate that the contribution of sensor advancements to thematic classification are highly dependent of spectral and spatial scene attributes.

Irons, J. R.↗
NASA NTRS1984

An integrated LANDSAT/ancillary data classification of desert rangeland

Range inventorying methods using LANDSAT MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, Utah. The vegetation is predominately desert shrub and annual grasses, with some annual forbs. Three LANDSAT scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Price, K. P.↗
NASA NTRS1985

An integrated Landsat/ancillary data classification of desert rangeland

Range inventorying methods using Landsat MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, UT. The vegetation is predominately desert shrub and annual grasses, with same annual forbs. Three Landsat scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Price, K. P.↗
NASA NTRS1994

Basalt generation at the Apollo 12 site. Part 1: New data, classification, and re-evaluation

New data are reported from five previously unanalyzed Apollo 12 mare basalts that are incorporated into an evaluation of previous petrogenetic models and classification schemes for these basalts. This paper proposes a classification for Apollo 12 mare basalts on the basis of whole-rock Mg# (molar 100*(Mg/(Mg+Fe))) and Rb/Sr ratio (analyzed by isotope dilution), whereby the ilmenite, olivine, and pigeonite basalt groups are readily distinguished from each other. Scrutiny of the Apollo 12 feldspathic 'suite' demonstrates that two of the three basalts previously assigned to this group (12031, 12038, 12072) can be reclassified: 12031 is a plagioclase-rich pigeonite basalt; and 12072 is an olivine basalt. Only basalt 12038 stands out as a unique sample to the Apollo 12 site, but whether this represents a single sample from another flow at the Apollo 12 site or is exotic to this site is equivocal. The question of whether the olivine and pigeonite basalt suites are co-magmatic is addressed by incompatible trace-element chemistry: the trends defined by these two suites when Co/Sm and Sm/Eu ratios are plotted against Rb/Sr ratio demonstrate that these two basaltic types cannot be co-magmatic. Crystal fractionation/accumulation paths have been calculated and show that neither the pigeonite, olivine, or ilmenite basalts are related by this process. Each suite requires a distinct and separate source region. This study also examines sample heterogeneity and the degree to which whole-rock analyses are representative, which is critical when petrogenetic interpretation is undertaken. Sample heterogeneity has been investigated petrographically (inhomogeneous mineral distribution) with consideration of duplicate analyses, and whether a specific sample (using average data) plots consistently upon a fractionation trend when a number of different compostional parameters are considered. Using these criteria, four basalts have been identified where reported analyses are not representative of the whole-rock composition: 12005, an ilmenite basalt; 12006 and 12036, olivine basalts; and 12031 previously classified as a feldspathic basalt, but reclassified as part of the pigeonite suite.

Neal, Clive R.↗
NASA NTRS1992

Decision fusion with reliabilities in multisource data classification

In this paper, a new multisource classifier which is based on a fusion of the class decisions of each separate data set is proposed. Each data set is separately fed into the local classifier and a final classification is performed by summarizing these local class decisions. An optimum decision fusion rule based on the minimum expected cost is derived. This new decision fusion rule can handle not only data set reliabilities but also classwise reliabilities of each data set. Classification experiments with two remotely sensed Thematic Mapper (TM) data sets show promising improvement over conventional multisource classification algorithms.

Jeon, Byeungwoo↗
NASA NTRS1974

Notes for the improvement of the spatial and spectral data classification method

This report examines the spatial and spectral clustering technique for the unsupervised automatic classification and mapping of earth resources satellite data, and makes theoretical analysis of the decision rules and tests in order to suggest how the method might best be applied to other flight data such as Skylab and Spacelab.

Dalton, C. C.↗
NASA NTRS2000

DMSP SSJ4 Data Restoration, Classification, and On-Line Data Access

Compress and clean raw data file for permanent storage We have identified various error conditions/types and developed algorithms to get rid of these errors/noises, including the more complicated noise in the newer data sets. (status = 100% complete). Internet access of compacted raw data. It is now possible to access the raw data via our web site, http://www.jhuapl.edu/Aurora/index.html. The software to read and plot the compacted raw data is also available from the same web site. The users can now download the raw data, read, plot, or manipulate the data as they wish on their own computer. The users are able to access the cleaned data sets. Internet access of the color spectrograms. This task has also been completed. It is now possible to access the spectrograms from the web site mentioned above. Improve the particle precipitation region classification. The algorithm for doing this task has been developed and implemented. As a result, the accuracies improved. Now the web site routinely distributes the results of applying the new algorithm to the cleaned data set. Mark the classification region on the spectrograms. The software to mark the classification region in the spectrograms has been completed. This is also available from our web site.

Wing, Simon↗
NASA NTRS1982

Using HCMM Thermal Data to Improve Classification of MSS Data

Spectral overlap between urban and rural land use/land cover categories can lead to unacceptable map accuracy levels in the classification of LANDSAT multispectral scanner (MSS) data. The four MSS bands used alone are not always adequate to distinguish among various land uses and cover types having similar spectral responses. The use of thermal data from the Heat Capacity Mapping Mission (HCMM) satellite as a means of improving MSS land cover classification accuracies for urban versus rural categories was investigated. The approaches used to integrate the HCMM data are described.

Witt, R. G.↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
NASA NTRS2009

Lossless Compression of Classification-Map Data

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Hua, Xie↗
NASA NTRS1998

Fitting a Two-Component Scattering Model to Polarimetric SAR Data

Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data. In this Paper an approach is described, which involves the fit of a combination of two simple scattering mechanisms to polarimetric SAR observations. The mechanisms am canopy scatter from a cloud of randomly oriented oblate spheroids, and a ground scatter term, which can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, seen through a layer of vertically oriented scatterers. An advantage of this model fit approach is that the scattering contributions from the two basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. The model fit can be applied to polarimetric AIRSAR data at C-, L- and P-Band.

Freeman, A.↗
NASA NTRS1996

A Three-component Scattering Model for Polarimetric SAR Data

Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data.

SAR data AIRSAR backscatter scattering↗
NASA NTRS2018

In Situ Measurements of Angular-Dependent Light Scattering by Aerosols over the Contiguous United States

This work provides a synopsis of aerosol phase function (F (sub 11)) and polarized phase function (F (sub 12)) measurements made by the Polarized Imaging Nephelometer (PINeph) during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and the Deep Convection Clouds and Chemistry (DC3) field campaigns. In order to more easily explore this extensive dataset, an aerosol classification scheme is developed that identifies the different aerosol types measured during the deployments. This scheme makes use of ancillary data that include trace gases, chemical composition, aerodynamic particle size and geographic location, all independent of PI-Neph measurements. The PI-Neph measurements are then grouped according to their ancillary data classifications and the resulting scattering patterns are examined in detail. These results represent the first published airborne measurements of F (sub 11) and minus F (sub 12) divided by F (sub 11) for many common aerosol types. We then explore whether PI-Neph light-scattering measurements alone are sufficient to reconstruct the results of this ancillary data classification algorithm. Principal component analysis (PCA) is used to reduce the dimensionality of the multi-angle PI-Neph scattering data and the individual measurements are examined as a function of ancillary data classification. Clear clustering is observed in the PCA score space, corresponding to the ancillary classification results, suggesting that, indeed, a strong link exists between the angular scattering measurements and the aerosol type or composition. Two techniques are used to quantify the degree of clustering and it is found that in most cases the results of the ancillary data classification can be predicted from PI-Neph measurements alone with better than 85 percent recall. This result both emphasizes the validity of the ancillary data classification as well as the PI-Neph's ability to distinguish common aerosol types without additional information.

Aerosol phase function↗
NASA NTRS1981

CROP type analysis using Landsat digital data

Classification and statistical sampling techniques for crop type discrimination using Landsat digital data have been developed by the University of California in cooperation with NASA and the California Department of Water Resources. Ratioed bands (MSS 7/5 and 5/4) and a sun-angle corrected Euclidean albedo band were prepared from data for the Sacramento Valley for five different dates. The test area was stratified into general crop groupings based on the particular patterns of irrigation timing for each crop. Data classified within each stratum were used to produce a crop type map. Comparison with ground data indicates that certain crops and crop groups are discernable. Small grains and rice are easily identifiable, as are deciduous fruit varieties as a group. However, it is not feasible to separate various fruit and nut varieties, or separate vegetable crops with these techniques at present.

Brown, C. E.↗
NASA NTRS1993

On evaluating the accuracy of SAR sea-ice classification using multifrequency polarimetric AIRSAR data

We investigate how multifrequency and polarimetric synthetic aperture radar (SAR) imagery enhances present capability to discriminate different ice conditions in single-frequency, single-polarization satellite SAR data. Frequencies considered are C- (lambda = 5.6cm), L- (lambda = 24cm) and P- (lambda = 68cm) band. Radar backscatter characteristics of six radiometrically and polarimetrically distinct ice types are selected from a cluster analysis of the multifrequency polarimetric SAR data and used to classify SAR images. Validation of these ice conditions is based on information provided by aerial photos, weather and ice surface measurements acquired at an ice camp, together with airborne passive microwave imagery, and visual analysis of the SAR data. The six identified sea-ice types are: (1) multiyear sea-ice; (2) compressed first year ice; (3) first year rubble and ridges; (4) first year rough ice; (5) first year smooth ice; and (6) thin ice. Open water is absent in all analyzed data. Classification of the SAR imagery into those six ice types is performed using a Bayesian Maximum A Posteriori classifier. Two complete scenes acquired at different dates in different locations are classified. The scenes were chosen such that they are representative of typical ice conditions in the Beaufort Sea in March 1988 and because ancillary information is available for validating the segmentation of various ice surface conditions.

Drinkwater, Mark R.↗
NASA NTRS1975

Joint Classification and Data Compression of multidimensional information sources - Application to ERTS

This paper describes a concept of Joint Classification and Data Compression of multidimensional information sources in the context of applications to Earth Resources Technology Satellites. The concept is used to define an Information Extraction System intended to remove data redundancy early in the sensor to user link in both classification and film interpretation applications. The approach uses cascaded partially supervised clustering to extract spectral intensity features from spatially local sources. Data compression is then used to efficiently represent spatial features within the spectral intensity feature map.

Hilbert, E. E.↗