Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “classification models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

LANDSAT data from agricultural sites: Crop signature analysis

The LANDSAT multispectral scanner (MSS) data were analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, was found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites were essentially two dimensional, and that the data from different sites and different acquisition lay on parallel planes in the four dimensional feature space. These results were exploited to gain new insight into the data and to develop alternate models for classification. In particular, it was found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗
NASA NTRS1977

Landsat data from agricultural sites - Crop signature analysis

The Landsat multispectral scanner (MSS) data have been analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, has been found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites are essentially two dimensional, and that the data from different sites and different acquisitions lie on parallel planes in the four-dimensional feature space. These results have been exploited to gain new insight into the data and to develop alternate models for classification. In particular, it has been found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗
NASA NTRS1975

Quantitative models of magnetic and electric fields in the magnetosphere

In order to represent the magnetic field B in the magnetosphere various auxiliary functions can be used: the current density, the scalar potential, toroidal and poloidal potentials, and Euler potentials -- or else, the components of B may be expanded directly. The most versatile among the linear representations is the one based on toroidal and poloidal potentials; it has seen relatively little use in the past but appears to be the most promising one for future work. Other classifications of models include simple testbed models vs. comprehensive ones and analytical vs. numerical representations. The electric field E in the magnetosphere is generally assumed to vary only slowly and to be orthogonal to B, allowing the use of a scalar potential which may be deduced from observations in the ionosphere, from the shape of the plasmapause, or from particle observations in synchronous orbits.

Stern, D. P.↗
NASA NTRS1973

Phase 1 of the earth resources data analysis program

Research completed in the Earth Resources Data Analysis Program is discussed along with recommendations for future study. Projects discussed include use of the Cholesky decomposition in feature selection and classification algorithms; optimal feature selection and extraction, probability density estimation and nonparametric classifiers; use of spatial information in classification; and model for crop row reflectance. The installation of LARSYS on the ICSA's IBM 370/155 is discussed, and a list of technical reports is included.

Source record↗
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 NTRS1980

Development of an information data base for watershed monitoring

Landsat multispectral scanner data, Defense Mapping Agency digital terrain data, conventional maps, and ground data were integrated to create a comprehensive information data base (the Image Based Information System), to monitor the water quality of the Lake Tahoe Basin. Landsat imagery was used as the planimetric base to which all other data were registered. A georeference image plane, which provided an interface between all data planes for the Lake Tahoe Basin data base, was created from the drainage basin map. The data base was used to extract each drainage basin for separate display. The Defense Mapping Agency-created elevation image was processed with VICAR software to produce a component representing slope magnitude, which was cross-tabulated with the drainage basin georeference table. Future applications of the data base include the development of precipitation modeling, surface runoff models, and classification of drainage basin cover types.

Smith, A. Y.↗
NASA NTRS1989

KL techniques for optimal processing of time sequential imagery

Time-sequential imagery is difficult to analyze because of its high dimensionality. This paper advances a new algorithm that screens input data in an intelligent way, discards data with negligible information, and uses the remaining images to represent the sequence in an optimal compact form. Data are presented to illustrate how this algorithm can be used to do novelty filtering, novelty detection, segmentation, background independent modeling, and classification.

Vermeulen, Pieter↗
NASA NTRS1991

Gravitational lenses: The current sample, recent results, and continuing searches

Gravitational lensing is one of the topics in astrophysics that was quite extensively discussed over time before it was actually discovered. Ten years after the discovery of the first one, it is interesting to note how the field has developed. After an initial slow rate of discovery, the last few years have seen an explosion in the number of reported cases. Attention was drawn to the first few cases because quasars at the same red shift, with similar optical spectra, were observed with angular separations of only a few arc seconds. Most observational effort has been devoted to searching for new candidate lens systems and carefully measuring their properties, both to test whether they are indeed lensed and to provide constraints for modeling. A classification of the lenses is into rings, arcs, multiples, and doubles, where the progression is from sources close to the optical axis to far from the optical axis. The known candidate systems are listed. The searches for gravitational lenses are proving to be successful, and more lenses continue to be discovered serendipitously. Many searches are under way, along with instruments that will routinely increase the resolution of astronomical imaging.

Hewitt, Jacqueline N.↗
NASA NTRS1989

Experiment and simulation for CSI: What are the missing links?

Viewgraphs on experiment and simulation for control structure interaction (CSI) are presented. Topics covered include: control structure interaction; typical control/structure interaction system; CSI problem classification; actuator/sensor models; modeling uncertainty; noise models; real-time computations; and discrete versus continuous.

Belvin, W. Keith↗
NASA NTRS1995

ROSAT observation program

In this annual progress report (November 1, 1993 to October 31, 1994), the geminga pulsar was observed by the ROSAT PSPC for 37,000 s in September 1993, in order to make a more detailed study than was previously possible of the pulse profile and two-component spectrum, and to do phase-resolved spectroscopy. This exposure was 2.5 times longer than the original discovery observation. In addition, a shorter 4,000 s exposure was made in October 1992, simultaneously with a GRO observation of Geminga, in order to verify the absolute phasing of the X-ray and gamma-ray peaks. We verified that the spectrum can be described as the sum of two black bodies, whose temperatures are 6 x 10(exp 5) K and 3 - 4 x 10(exp 6) K, with the latter covering 3 x 10(exp -5) the area of the former. The pulse profiles indicate that the intensity of the two emitting regions peak is approximately 90 deg out of phase in rotation, but that the temperatures are otherwise independent of phase. An improved estimate of the distance can be made from the cooler (larger) blackbody component, yielding d = 440 +/- 120 pc. Another program of this report was to obtain PSPC spectra of an important class of Seyfert galaxies which have narrow lines and stron permitted Fe II emission. Sometines called I Zw 1 objects, or narrow-line Seyfert 1s, they are crucial to our understanding of Seyfert classification and models of Seyfert unification. We observed four new objects and, in addition, obtained data on 17 more from the ROSAT archive. A third program combined PSPC and HRI observations of selected Seyfert galaxies which have unusual and variable spectra. The purpose was to disentangle diffuse X-ray emission from the nuclear source, in order to properly interpret the soft X-ray spectral shapes in terms of partial covering and/or warm-absorber models. The targets of the program are NGC 3516, NGC 3227, and NGC 7314. So far, we have only performed a dtailed analysis on NGC 3516. The fourth program of this report is the NGC 1672, the second brightest Seyfert galaxy and one or the original 'composite' Seyfert/starburst galaxies which have evidence for both Seyfert activity and H II regions in their optical spectra. It is one of the lowest luminosity Seyfert 2 galaxies that can be studied in detail.

Halpern, Jules P.↗
NASA NTRS1995

Accurate estimation of sigma(exp 0) using AIRSAR data

During recent years signature analysis, classification, and modeling of Synthetic Aperture Radar (SAR) data as well as estimation of geophysical parameters from SAR data have received a great deal of interest. An important requirement for the quantitative use of SAR data is the accurate estimation of the backscattering coefficient sigma(exp 0). In terrain with relief variations radar signals are distorted due to the projection of the scene topography into the slant range-Doppler plane. The effect of these variations is to change the physical size of the scattering area, leading to errors in the radar backscatter values and incidence angle. For this reason the local incidence angle, derived from sensor position and Digital Elevation Model (DEM) data must always be considered. Especially in the airborne case, the antenna gain pattern can be an additional source of radiometric error, because the radar look angle is not known precisely as a result of the the aircraft motions and the local surface topography. Consequently, radiometric distortions due to the antenna gain pattern must also be corrected for each resolution cell, by taking into account aircraft displacements (position and attitude) and position of the backscatter element, defined by the DEM data. In this paper, a method to derive an accurate estimation of the backscattering coefficient using NASA/JPL AIRSAR data is presented. The results are evaluated in terms of geometric accuracy, radiometric variations of sigma(exp 0), and precision of the estimated forest biomass.

Holecz, Francesco↗
NASA NTRS2020

Lower Omo Food Security & Agriculture: Mapping Land Cover Change in Unprotected and Protected Areas in the Lower Omo River Valley, Ethiopia

Ethiopia is home to unique wildlife, biodiversity, and ecosystem services and, like much of the world, is undergoing population growth, development, and land use change. As a result, some biodiverse regions may be at risk of being urbanized, cultivated as agricultural plots, or losing access to water bodies that are essential for maintaining both terrestrial and aquatic life. The DEVELOP team partnered with the Ethiopian Wildlife Conservation Authority to quantify the land cover change between the years 1994, 2010, and 2018. The team utilized Ethiopia’s dry season (January to May) for training point development which was crucial in differentiating the level of greenness between the four land cover classes: water, natural vegetation, cultivated land, and bare ground. The study area covered 62,000 km2 of the Lower Omo River Valley and includes eight protected areas. Data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Shuttle Radar Topography Mission were used to employ a Random Forest classifier and identify the four classes within Google Earth Engine. For each of the supervised classifications, overall model accuracy was between 83% (2018) and 89% (1994). Between 1994 and 2018, the Lower Omo Valley experienced an overall increase of 258% (919 km2) in water and 291% (7,188 km2) in cultivated areas, while experiencing a 9% (2,761 km2) decrease in natural vegetation and 19% (5,346 km2) bare ground. There was an increase in water and cultivated land and a decrease in natural vegetation and bare ground in unprotected areas and all protected areas. However, protected areas maintained natural vegetation better than unprotected areas and only experienced a 1% (59 km2) loss compared to a 10% (2,701 km2) loss in unprotected areas.

Food Security & Agriculture↗
NASA NTRS2020

Lower Omo Food Security & Agriculture: Mapping Land Cover Change in Unprotected and Protected Areas in the Lower Omo River Valley, Ethiopia

Ethiopia is home to unique wildlife, biodiversity, and ecosystem services and, like much of the world, is undergoing population growth, development, and land use change. As a result, some biodiverse regions may be at risk of being urbanized, cultivated as agricultural plots, or losing access to water bodies that are essential for maintaining both terrestrial and aquatic life. The DEVELOP team partnered with the Ethiopian Wildlife Conservation Authority to quantify the land cover change between the years 1994, 2010, and 2018. The team utilized Ethiopia’s dry season (January to May) for training point development which was crucial in differentiating the level of greenness between the four land cover classes: water, natural vegetation, cultivated land, and bare ground. The study area covered 62,000 sq.km of the Lower Omo River Valley and includes eight protected areas. We used Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Shuttle Radar Topography Mission imagery within Google Earth Engine to employ a Random Forest Classifier and identify these four distinct classes. For each of our supervised classifications, overall model accuracy was between 83% (2018) and 89% (1994). Between 1994 and 2018, the Lower Omo Valley experienced an overall increase of 258.57% in water bodies and 291.23% in cultivated areas, while experiencing an 8.91% decrease in natural vegetation and 18.76% bare ground. There was an increase in water bodies and cultivated land and a decrease in natural vegetation and bare ground in unprotected areas and all protected areas. However, protected areas disproportionately experienced an increase in land cover change, including Tama Community Conservation Area which saw a 17,272.33% increase between 1994 and 2018.

Food Security & Agriculture↗
NASA NTRS1984

Use of Landsat-derived profile features for spring small-grains classification

The present model for the temporal behavior of agricultural greenness is applied to the extraction of Landsat-derived profile features, distinguishing small from large grain crops. An additional feature derivable from the temporal behavior of the ratio of greenness to brightness is noted which aids in the separation of crops from other vegetation. A limited training set of 20 pure pixels/class, obtained from ground data, is subjected to the Ho-Kashyap (1965) linear classifier. The initial correct classification value for pure pixels of about 85 percent drops to 75 percent for all Landsat pixels.

Badhwar, G. D.↗
NASA NTRS1982

Use of Landsat-derived temporal profiles for corn-soybean feature extraction and classification

A physical model is presented, which has been derived from multitemporal-multispectral data acquired by Landsat satellites to describe the behavior and new features that are crop specific. A feasibility study over 40 sites was performed to classify the segment pixels into those of corn, soybeans, and others using the new features and a linear classifier. Results agree well with other existing methods, and it is shown the multitemporal-multispectral scanner data can be transformed into two parameters that are closely related to the target of interest and thus can be used in classification. The approach is less time intensive than other techniques and requires labeling of only pure pixels.

Badhwar, G. D.↗
NASA NTRS1991

Spatio-temporal contextual classification based on Markov random field model

A contextural classifier based on a Markov random field model, which can utilize both spatial and temporal contexts, is investigated. Spatial and temporal neighbors are defined, and the class assignment of each pixel is assumed to be dependent only on the measurement vectors of itself and those of its spatial and temporal neighbors according to the Markov random field property. Only interpixel class dependency context is used in the classification. The joint prior probability of the classes of each pixel and its spatial and temporal neighbors are modeled by a Gibbs random field. The classification is performed in a recursive manner. Experiments with multi-temporal Thematic Mapper data show promising results.

Jeon, Byeungwoo↗
NASA NTRS1989

A hybrid architecture for the implementation of the Athena neural net model

The implementation of an earlier introduced neural net model for pattern classification is considered. Data flow principles are employed in the development of a machine that efficiently implements the model and can be useful for real time classification tasks. Further enhancement with optical computing structures is also considered.

Koutsougeras, C.↗