Search NASA⌕ Search

SEARCH · Search NASA

Results for “radar classification”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Mapping Vegetative Ground Cover From Polarimetric SAR Data

Three-frequency quad-polarization data used to distinguish among different types of cover. MAPVEG computer program performs first-level classification of NASA/JPL AIRSAR polarimetric synthetic-aperture-radar (SAR) data into simple ground-cover types. Classification images generated used to separate vegetated from nonvegetated areas; useful in determining soil moisture in areas free of vegetation. Classification separates forests from surrounding vegetation, and used to identify clear-cut areas. Urban areas and potential marshlands examples of other identifiable classes. Written in FORTRAN 77.

Chapman, Bruce↗
NASA NTRS1999

TRMM Precipitation Radar Reflectivity Profiles Compared to High-Resolution Airborne and Ground-Based Radar Measurements

In this paper, TRMM (Tropical Rainfall Measuring Mission Satellite) Precipitation Radar (PR) products are evaluated by means of simultaneous comparisons with data from the high-altitude ER-2 Doppler Radar (EDOP), as well as ground-based radars. The comparison is aimed primarily at the vertical reflectivity structure, which is of key importance in TRMM rain type classification and latent heating estimation. The radars used in this study have considerably different viewing geometries and resolutions, demanding non-trivial mapping procedures in common earth-relative coordinates. Mapped vertical cross sections and mean profiles of reflectivity from the PR, EDOP, and ground-based radars are compared for six cases. These cases cover a stratiform frontal rainband, convective cells of various sizes and stages, and a hurricane. For precipitating systems that are large relative to the PR footprint size, PR reflectivity profiles compare very well to high-resolution measurements thresholded to the PR minimum reflectivity, and derived variables such as bright band height and rain types are accurate, even at high PR incidence angles. It was found that for, the PR reflectivity of convective cells small relative to the PR footprint is weaker than in reality. Some of these differences can be explained by non-uniform beam filling. For other cases where strong reflectivity gradients occur within a PR footprint, the reflectivity distribution is spread out due to filtering by the PR antenna illumination pattern. In these cases, rain type classification may err and be biased towards the stratiform type, and the average reflectivity tends to be underestimated. The limited sensitivity of the PR implies that the upper regions of precipitation systems remain undetected and that the PR storm top height estimate is unreliable, usually underestimating the actual storm top height. This applies to all cases but the discrepancy is larger for smaller cells where limited sensitivity is compounded by incomplete beam filling. Users of level three TRMM PR products should be aware of this scale dependency.

Heymsfield, G. M.↗
NASA NTRS1985

Detection of lowland flooding using active microwave systems

The development of radar systems with longer wavelenths (greater than 3 cm) has provided new possibilities regarding the utilization of radar. Thus, it has been found that the interpretation of data from radar images can be a valuable classification aid for applications related to water resources. In the case of an interpreter accustomed to photographic or visible/infrared images, an evaluation of radar images presents some problems, because the radar is sensing a set of surface characteristics which have little influence on visible/infrared systems. Detectable features in radar images caused by differences in dielectric properties are usually associated with the water content of either soils or vegetation. The present paper is concerned with studies which were initiated in 1976. The studies had the objective to define the magnitude of the effects on radar data caused by flood waters under vegetation. The obtained results indicate the feasibility to detect flood conditions beneath a forest canopy, and to obtain an improved definition of the land-water boundary.

Ormsby, J. P.↗
NASA NTRS1983

An exploitation of coregistered SIR-A, Seasat and Landsat images

Multispectral registration and classification of SIR-A, Seasat SAR, and Landsat MSS data is presented over two playas located in the northeastern Algerian Sahara. A supervised classification was made over six classes: salt, palm trees, dunes, limestones, gypsum and sand. The best classification is obtained by using all of the data. The images using radar only misclassify trees and salt, limestone and dunes, gypsum and dunes. Landsat only gives a good map but lacks the roughness information contained in the radar data. The Landsat/SIR-A combination gives a better classification than the Landsat/Seasat combination. Density number histograms computed within several classes on the Seasat and SIR-A data show the misclassification is mainly due to the Seasat data.

Rebillard, P.↗
NASA NTRS1992

Can a time-stratigraphic classification system be developed for Venus?

Magellan radar images reveal that Venus' exposed geologic record covers a relatively short and recent time span, as indicated by the low density of impact craters across the planet. Therefore, because impact cratering in itself will not be a useful tool to define geologic ages on Venus, it was questioned whether a useful stratigraphic scheme can be developed for the planet. We believe that a venusian stratigraphy is possible and that it can be based on the following: (1) an examination of the rationale and methods that have been used to develop such schemes for the other planets; and (2) what can be gleaned from Magellan and other datasets of Venus.

Tanaka, K. L.↗
NASA NTRS2000

Fusion of AIRSAR and TM Data for Parameter Classification and Estimation in Dense and Hilly Forests

The expanded remotely sensed data space consisting of coincident radar backscatter and optical reflectance data provides for a more complete description of the Earth surface. This is especially useful where many parameters are needed to describe a certain scene, such as in the presence of dense and complex-structured vegetation or where there is considerable underlying topography. The goal of this paper is to use a combination of radar and optical data to develop a methodology for parameter classification for dense and hilly forests, and further, class-specific parameter estimation. The area to be used in this study is the H. J. Andrews Forest in Oregon, one of the Long-Term Ecological Research (LTER) sites in the US. This area consists of various dense old-growth conifer stands, and contains significant topographic relief. The Andrews forest has been the subject of many ecological studies over several decades, resulting in an abundance of ground measurements. Recently, biomass and leaf-area index (LAI) values for approximately 30 reference stands have also become available which span a large range of those parameters. The remote sensing data types to be used are the C-, L-, and P-band polarimetric radar data from the JPL airborne SAR (AIRSAR), the C-band single-polarization data from the JPL topographic SAR (TOPSAR), and the Thematic Mapper (TM) data from Landsat, all acquired in late April 1998. The total number of useful independent data channels from the AIRSAR is 15 (three frequencies, each with three unique polarizations and amplitude and phase of the like-polarized correlation), from the TOPSAR is 2 (amplitude and phase of the interferometric correlation), and from the TM is 6 (the thermal band is not used). The range pixel spacing of the AIRSAR is 3.3m for C- and L-bands and 6.6m for P-band. The TOPSAR pixel spacing is 10m, and the TM pixel size is 30m. To achieve parameter classification, first a number of parameters are defined which are of interest to ecologists for forest process modeling. These parameters include total biomass, leaf biomass, LAI, and tree height. The remote sensing data from radar and TM are used to formulate a multivariate analysis problem given the ground measurements of the parameters. Each class of each parameter is defined by a probability density function (pdf), the spread of which defines the range of that class. High classification accuracy results from situations in which little overlap occurs between pdfs. Classification results provide the basis for the future work of class-specific parameter estimation using radar and optical data. This work was performed in part by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, and in part by the NASA Ames Research Center, Moffett Field, CA, both under contract from the National Aeronautics and Space Administration.

Moghaddam, Mahta↗
NASA NTRS1985

What are the best radar wavelengths, incidence angles and polarizations for geologic applications? A statistical approach

Linear discriminant analysis of multifrequency and multipolarization radar scatterometer data of lava flows and sedimentary rocks indicates that the lava flows can be separated by age and the sedimentary rocks can be discriminated from one another. The optimum wavelengths, polarizations and incidence angles among those available for these problems was determined by the discriminant analysis program. For separation of the lava flows, shorter wavelengths, smaller incidence angles and horizontal polarization are best. A SIR-C radar configuration could provide nearly complete discrimination of these lava flows. Conversely, the longer wavelengths, larger incidence angles and vertical polarization was preferred for sedimentary rocks, perhaps due to the slight vegetation cover. Satisfactory classification of sedimentary rocks requires more radar data than for the lavas. These results are potentially useful both for radar system configuration and for geological applications. The method developed here may provide a rationale for user specification of imaging system parameters.

Blom, R.↗
NASA NTRS1984

Use of multi-frequency, multi-polarization, multi-angle airborne radars for class discrimination in a southern temperature forest

The utility of radar scatterometers for discrimination and characterization of natural vegetation was investigated. Backscatter measurements were acquired with airborne multi-frequency, multi-polarization, multi-angle radar scatterometers over a test site in a southern temperate forest. Separability between ground cover classes was studied using a two-class separability measure. Very good separability is achieved between most classes. Longer wavelength is useful in separating trees from non-tree classes, while shorter wavelength and cross polarization are helpful for discrimination among tree classes. Using the maximum likelihood classifier, 50% overall classification accuracy is achieved using a single, short-wavelength scatterometer channel. Addition of multiple incidence angles and another radar band improves classification accuracy by 20% and 50%, respectively, over the single channel accuracy. Incorporation of a third radar band seems redundant for vegetation classification. Vertical transmit polarization is critically important for all classes.

Mehta, N. C.↗
NASA NTRS1993

Venus small volcano classification and description

The high resolution and global coverage of the Magellan radar image data set allows detailed study of the smallest volcanoes on the planet. A modified classification scheme for volcanoes less than 20 km in diameter is shown and described. It is based on observations of all members of the 556 significant clusters or fields of small volcanoes located and described by this author during data collection for the Magellan Volcanic and Magmatic Feature Catalog. This global study of approximately 10 exp 4 volcanoes provides new information for refining small volcano classification based on individual characteristics. Total number of these volcanoes was estimated to be 10 exp 5 to 10 exp 6 planetwide based on pre-Magellan analysis of Venera 15/16, and during preparation of the global catalog, small volcanoes were identified individually or in clusters in every C1-MIDR mosaic of the Magellan data set. Basal diameter (based on 1000 measured edifices) generally ranges from 2 to 12 km with a mode of 34 km, and follows an exponential distribution similar to the size frequency distribution of seamounts as measured from GLORIA sonar images. This is a typical distribution for most size-limited natural phenomena unlike impact craters which follow a power law distribution and continue to infinitely increase in number with decreasing size. Using an exponential distribution calculated from measured small volcanoes selected globally at random, we can calculate total number possible given a minimum size. The paucity of edifice diameters less than 2 km may be due to inability to identify very small volcanic edifices in this data set; however, summit pits are recognizable at smaller diameters, and 2 km may represent a significant minimum diameter related to style of volcanic eruption. Guest, et al, discussed four general types of small volcanic edifices on Venus: (1) small lava shields; (2) small volcanic cones; (3) small volcanic domes; and (4) scalloped margin domes ('ticks'). Steep-sided domes or 'pancake domes', larger than 20 km in diameter, were included with the small volcanic domes. For the purposes of this study, only volcanic edifices less than 20 km in diameter are discussed. This forms a convenient cutoff since most of the steep-sided domes ('pancake domes') and scalloped margin domes ('ticks') are 20 to 100 km in diameter, are much less numerous globally than are the smaller diameter volcanic edifices (2 to 3 orders of magnitude lower in total global number), and do not commonly occur in large clusters or fields of large numbers of edifices.

Aubele, J. C.↗
NASA NTRS1992

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

Degrandi, G.↗
NASA NTRS1984

Venus: Global distribution of Pioneer-Venus radar roughness and reflectivity and global correlation of altimetry, roughness and reflectivity observations

An improved version of the Pionner-Venus orbital data was used for a statistical analysis of global radar roughness and (alpha(0)) (rho) reflectivity. Classification maps of the venusian surface are produced in a supervised manner on the basis of statistical and empirical studies of the individual data sets. The primary objective is to assess the degree of homogeneity of surface radar properties within topographic provinces in order to map possible geologic boundaries. Maps were produced by correlating two data sets at a time. Classification of specific regions, such as Ishtar, has demonstrated that distinct geological units can be identified.

Head, J. W.↗
NASA NTRS1990

Multiband radar characterization of forest biomes

The utility of airborne and orbital SAR in classification, assessment, and monitoring of forest biomes is investigated through analysis of orbital synthetic aperature radar (SAR) and multifrequency and multipolarized airborne SAR imagery relying on image tone and texture. Preliminary airborne SAR experiments and truck-mounted scatterometer observations demonstrated that the three dimensional structural complexity of a forest, and the various scales of temporal dynamics in the microwave dielectric properties of both trees and the underlying substrate would severely limit empirical or semi-empirical approaches. As a consequence, it became necessary to develop a more profound understanding of the electromagnetic properties of a forest scene and their temporal dynamics through controlled experimentation coupled with theoretical development and verification. The concatenation of various models into a physically-based composite model treating the entire forest scene became the major objective of the study as this is the key to development of a series of robust retrieval algorithms for forest biophysical properties. In order to verify the performance of the component elements of the composite model, a series of controlled laboratory and field experiments were undertaken to: (1) develop techniques to measure the microwave dielectric properties of vegetation; (2) relate the microwave dielectric properties of vegetation to more readily measured characteristics such as density and moisture content; (3) calculate the radar cross-section of leaves, and cylinders; (4) improve backscatter models for rough surfaces; and (5) relate attenuation and phase delays during propagation through canopies to canopy properties. These modeling efforts, as validated by the measurements, were incorporated within a larger model known as the Michigan Microwave Canopy Scattering (MIMICS) Model.

Dobson, M. Craig↗
NASA NTRS1997

On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

Medina Revera, Edwin J.↗
NASA NTRS1993

Radar response of vegetation: An overview

This document contains a number of viewgraphs on surface and vegetation backscattering. A classification of vegetation based on general scattering properties is presented. Radar scattering mechanisms are discussed, and backscattering and reflection coefficients for soil back scattering models are given. Radar response to vegetation is presented, with the objectives to discriminate and classify vegetation; to estimate biomass, leaf area index (LAI), and soil moisture; and to monitor changes, including deforestation and growth. Both theory and observation (laboratory, field, air SAR, and European Remote Sensing Satellite (ERS-1) observations) are used to present backscatter coefficients and other data for various vegetation types. ERS-1 results include class statistics, comparison with theory, and biomass response and seasonal variation (LAI) for deciduous and coniferous forests.

Ulaby, Fawwaz T.↗
NASA NTRS2017

Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

Monsoon↗
NASA NTRS1999

Mapping Vegetation Structure in Kakadu National Park: An AIRSAR and GIS Application in Conservation

Airborne Synthetic Aperture Radar (AIRSAR) data were used to map vegetation structure in Kakadu National Park Australia as part of the PACRIM project. SAR data were co-registered with Landsat TM, aerial photos, and map data in a geographic information system for a small test area consisting of mangrove, floodplain grasslands, lowland tropical evergreen forest and upland mixed deciduous and evergreen tropical forest near the South Alligator River. Landsat (Thematic Mapper) TM very clearly showed the floristic composition and burn scars from the previous years fires and the AIRSAR data provided a profile of vegetation structure. Extensive field data on vegetation species composition and structure were collected across a series of transects in cooperation with a survey of avifauna in an effort to link the habitat edge structure with bird species responses. A test site was found that contained two types of habitat edges: 1) A structure specific edge - characterized by the appearance of a very strong structural change in the forest canopy occurring in the absence of a substantial turnover in floristics. 2) Floristic edge - a sharp transition in vegetation genetic composition with a mixed set of structural changes. Specific polarization combinations were selected that were highly correlated to a set of desired structural parameters found in the field data. Classification routines were employed to group radar pixels into 3 structural classes based on: the Surface Area to Volume ratio (SA/V) of the stems, the SA/V of the branches, and the leaf area index of the canopy. Separate canopy structure maps were then entered into the GIS and bird responses were observed relative to the classes and their boundaries. Follow-on work will consist of extending this approach to neighboring areas, generating structure maps, predicting bird responses across the edges, and make accuracy assessments.

Imhoff, Marc L.↗
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↗