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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Per-point and per-field contextual classification of multipolarization and multiple incidence angle aircraft L-band radar data

Multipolarized aircraft L-band radar data are classified using two different image classification algorithms: (1) a per-point classifier, and (2) a contextual, or per-field, classifier. Due to the distinct variations in radar backscatter as a function of incidence angle, the data are stratified into three incidence-angle groupings, and training and test data are defined for each stratum. A low-pass digital mean filter with varied window size (i.e., 3x3, 5x5, and 7x7 pixels) is applied to the data prior to the classification. A predominately forested area in northern Florida was the study site. The results obtained by using these image classifiers are then presented and discussed.

Hoffer, Roger M.↗
NASA NTRS2020

An Evaluation of Clouds and Radiation in a Large-Scale Atmospheric Model Using a Cloud Vertical Structure Classification

We revisit the concept of the cloud vertical structure (CVS) classes we have previously employed to classify the planet's cloudiness (Oreopoulos et al., 2017). The CVS classification reflects simple combinations of simultaneous cloud occurrence in the three standard layers traditionally used to separate low, middle, and high clouds and was applied to a dataset derived from active lidar and cloud radar observations. This classification is now introduced in an atmospheric global climate model, specifically a version of NASA's GEOS-5, in order to evaluate the realism of its cloudiness and of the radiative effects associated with the various CVS classes. Such classes can be defined in GEOS-5 thanks to a sub column cloud generator paired with the model's radiative transfer algorithm, and their associated radiative effects can be evaluated against observations. We find that the model produces 50% more clear skies than observations in relative terms and produces isolated high clouds that are slightly less frequent than in observations, but optically thicker, yielding excessive planetary and surface cooling. Low clouds are also brighter than in observations, but underestimates of the frequency of occurrence (by ~20% in relative terms) help restore radiative agreement with observations. Overall the model better reproduces the longwave radiative effects of the various CVS classes because cloud vertical location is substantially constrained in the CVS framework.

Lee, Dongmin↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
NASA NTRS1989

Active microwave classification of sea ice

Radar backscatter studies of Arctic sea ice have been carried out over a number of years with the intent to acquire physical property information through the examination of microwave signatures. The breadth of these studies continues to expand; as an example, measurements are now conducted at frequencies from 500 MHz to about 100 GHz. One of the scientific goals of this work has been to develop an improved outstanding of the scattering processes at play. A second, equally important goal has been to apply the knowledge gained in examining the backscatter response of ice and snow made in conjunction with the detailed scene characterizations, the insight gained through theoretical modeling and parametric study, and the data entered into the radar signature library to develop procedures to convert microwave signal information (available in the very near future) into valuable data products. This should ultimately provide a better understanding of the environment. The author discusses what has been learned through the many efforts associated with the near-surface scatterometer measurement programs and how the knowledge gained is assisting in the development of future sea ice type satellite algorithms. The logic and mechanisms used in discriminating sea ice types are presented.

Onstott, Robert G.↗
NASA NTRS1990

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Kong, Jin AU↗
NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗
NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗
NASA NTRS1990

Coronae on Venus - Morphology, classification, and distribution

Venera 15/16 radar images of Venus show two circular features having no analog among the terrestrial planets: 21 coronae, which have a complex interior zone, and 11 corona-like features, which lack the ridge annuli of the coronae. Each of these two groups of features is subdivisible into three classes; coronae may be symmetrical, asymmetrical, and subdued, while corona-like structures are double, irregular, and heart-shaped. The various classes of both types of classes are interpreted as having a similar mode of origin, namely the uplift and volcanism generated by a thermal anomaly at depth, followed by gravitational relaxation.

Pronin, A. A.↗
NASA NTRS2004

Classification of Tropical Oceanic Precipitation using High Altitude Aircraft: Microwave and Electric Field Measurements

During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the National Aeronautics and Space Administration ER-2 high altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35,37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower altitude horizontal reflectivity scans collected by the National Oceanic and Atmospheric Administration WP-3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

Hood, Robbie E.↗
NASA NTRS2004

Classification of Tropical Oceanic Precipitation using High-Altitude Aircraft Microwave and Electric Field Measurements

During the 1998 and 2001 hurricane seasons of the western Atlantic Ocean and Gulf of Mexico, the Advanced Microwave Precipitation Radiometer (AMPR), the ER-2 Doppler (EDOP) radar, and the Lightning Instrument Package (LIP) were flown aboard the NASA ER-2 high-altitude aircraft as part of the Third Convection and Moisture Experiment (CAMEX-3) and the Fourth Convection and Moisture Experiment (CAMEX-4). Several hurricanes, tropical storms, and other precipitation systems were sampled during these experiments. An oceanic rainfall screening technique has been developed using AMPR passive microwave observations of these systems collected at frequencies of 10.7, 19.35, 37.1, and 85.5 GHz. This technique combines the information content of the four AMPR frequencies regarding the gross vertical structure of hydrometeors into an intuitive and easily executable precipitation mapping format. The results have been verified using vertical profiles of EDOP reflectivity and lower-altitude horizontal reflectivity scans collected by the NOAA WP3D Orion radar. Matching the rainfall classification results with coincident electric field information collected by the LIP readily identifies convective rain regions within the precipitation fields. This technique shows promise as a real-time research and analysis tool for monitoring vertical updraft strength and convective intensity from airborne platforms such as remotely operated or uninhabited aerial vehicles. The technique is analyzed and discussed for a wide variety of precipitation types using the 26 August 1998 observations of Hurricane Bonnie near landfall.

Hood, Robbie E.↗
NASA NTRS2020

Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets

As one of the world’s largest freshwater ecosystems,the Great Lakes Basin houses hundreds of thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global levels.Monitoring these wetlands is critical to conservation and restoration efforts, however current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET),which allows semi-automatic wetland classification according to a user-input area of interest and date range. WET composites datasets and conducts multi source, moderate resolution processing utilizing Landsat 8 OLI, Sentinel-2 MSI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope,or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that elevation produced the most accurate classification in Lake Erie, Michigan,and Ontario, while slope performed best in Lake Huron and Superior. Lake Erie, Michigan, Ontario, and Huron achieved high overall accuracy and identification of wetlands. WET leverages cloud-computing for multi source processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Vanessa L Valenti↗
NASA NTRS1976

Radar system specifications for hydrology and agriculture

Using truck-mounted radar spectrometers, an experimental program was conducted from 1971 to 1976 to define radar system specifications for hydrological and agricultural applications. Data were acquired over the 1-18 GHz region, 0 deg (nadir) to 70 deg range and for all linear polarization combinations. The discussion here concerns radar parameters for crop classification and for soil moisture determination.

Ulaby, F. T.↗