Search NASA⌕ Search

SEARCH · Search NASA

Results for “classifier 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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola

Following the end of the Angolan Civil War (1975-2002), human habitation in Iona National Park has grown exponentially, as has the livestock population. An ongoing drought beginning in 2017 has brought people, livestock, and wildlife into increasing competition for resources within the park. This study used Earth observation data, primarily Landsat and Sentinel imagery, to examine landscape trends to improve wildlife preservation approaches in Iona National Park, Angola. In collaboration with the NGO African Parks, we developed a robust land use and land cover (LULC) classification model using remote sensing data to augment sparse ground-based data in this arid land region. We used Google Earth Engine and a random forest classifier to map vegetation types, water bodies, and potential wildlife habitats. This analysis resulted in a high spatial resolution LULC time-series between 1984-2023, highlighting critical periods of socioecological change over the past 40 years. These results increased the partner’s ability to make scientifically grounded decisions about resource allocation and conservation priorities. This analysis supports the feasibility of applying remote sensing techniques coupled with machine learning models in dry regions, where standard survey methods are frequently limited by accessibility and resource availability. However, we identified limitations in ground-truth data and the difficulty of recognizing certain vegetation types in arid areas. Despite these limitations, the study demonstrated Earth observations' ability to transform wildlife management techniques in distant and data-scarce locations, providing a reproducible foundation for similar ecosystems around the world.

Emmanuel Aklie↗
NASA NTRS2010

Assessment of Aerosol Distributions from GEOS-5 Using the CALIPSO Feature Mask

A-train sensors such as MODIS, MISR, and CALIPSO are used to determine aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important for climate assessment, air quality applications, and for comparisons and analysis with aerosol transport models. The Aerosols-Clouds-Ecosystems (ACE) satellite mission proposed in the NRC Decadal Survey describes a next generation aerosol and cloud suite similar to the current A-train, including a lidar. The future ACE lidar must be able to determine aerosol type effectively in conjunction with modeling activities to achieve ACE objectives. Here we examine the current capabilities of CALIPSO and the NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-5), to place future ACE needs in context. The CALIPSO level 2 feature mask includes vertical profiles of aerosol layers classified by type. GEOS-5 provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures and extinction profiles along the CALIPSO orbit track. In previous work, initial comparisons between GEOS-5 derived aerosol mixtures and CALIPSO derived aerosol types were presented for July 2007. In general, the results showed that model and lidar derived aerosol types did not agree well in the boundary layer. Agreement was poor over Europe, where CALIPSO indicated the presence of dust and pollution mixtures yet GEOS-5 was dominated by pollution with little dust. Over the ocean in the tropics, the model appeared to contain less sea salt than detected by CALIPSO, yet at high latitudes the situation was reserved. Agreement between CALIPSO and GEOS-5, aerosol types improved above the boundary layer, primarily in dust and smoke dominated regions. At higher altitudes (> 5 km), the model contained aerosol layers not detected by CALIPSO. Here we present new results for a full year study using the new Version 3 CALIPSO data and most recent GEOS-5 model results.

Welton, Ellsworth↗
NASA NTRS2019

An Approach for Uncertainty Quantification and Management of Unmanned Aerial Vehicle Health

The increasing interest in low-altitude unmanned aerial vehicle (UAV) operations is bringing along safety concerns. Performance of small, low-cost UAVs drastically changes with type, size and controller of the vehicle. Their reliability is lower when compared to reliability of commercial aircrafts, and the availability of on-board sensors for health and state awareness is extremely limited due to their size and propulsion capabilities. Uncertainty plays a dominant role in such a scenario, where a variety of UAVs of different size, propulsion systems, dynamic performance and reliability enters the low-altitude airspace. Unexpected failures could have dangerous consequences for both equipment and humans within that same airspace. As a result, a number of research works and methodologies are being proposed in the area of UAV dynamic modeling, health and safety monitoring, but uncertainty quantification is rarely addressed. Thus, this paper pro- poses a perspective towards uncertainty quantification for autonomous systems, giving special emphasis to a UAV health monitoring application. A formal approach to classify uncertainty is presented; it is utilized to identify the uncertainty sources in UAVs health and operations, and then map uncertainty within a predictive process. To show the application of the methodology proposed here, the design of a model-based powertrain health monitoring algorithm for small-size UAVs is used as case study. The example illustrates how the uncertainty quantification approach can help the modeling strategy, as well as the assessment of diagnostic and prognostic performance.

Health Monitoring↗
NASA NTRS1982

System support documentation: IDIMS FUNCTION AMOEBA

A listing is provided for AMOEBA, a clustering program based on a spatial-spectral model for image data. The program is fast and automatic (in the sense that no parameters are required), and classifies each picture element into classes which are determined internally. As an IDIMS function, no limit on the size of the image is imposed.

Bryant, J.↗
NASA NTRS1981

Use of collateral information to improve LANDSAT classification accuracies

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Strahler, A. H.↗
NASA NTRS1984

Power spectral ensity of markov texture fields

Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.

Shanmugan, K. S.↗
NASA NTRS1985

Apollo 15 mare units and their petrogenesis

Samples from 12 different mare sites were identified and classified among the Apollo 15 samples. The genesis of the Apollo 15 mare units is summarized given the general model of mare basalt source regions and of more basalt genesis derived from a synthesis of the major oxide/major mineral, compatible siderophile, and incompatible trace element data and isotopic ratios of the Rb/Sr and Sm/Nd systems.

Binder, A. A.↗
NASA NTRS1989

Annotated bibliography of software engineering laboratory literature

An annotated bibliography is presented of technical papers, documents, and memorandums produced by or related to the Software Engineering Laboratory. The bibliography was updated and reorganized substantially since the original version (SEL-82-006, November 1982). All materials were grouped into eight general subject areas for easy reference: (1) The Software Engineering Laboratory; (2) The Software Engineering Laboratory: Software Development Documents; (3) Software Tools; (4) Software Models; (5) Software Measurement; (6) Technology Evaluations; (7) Ada Technology; and (8) Data Collection. Subject and author indexes further classify these documents by specific topic and individual author.

Buhler, Melanie↗
NASA NTRS2000

BOREAS TE-18 Landsat TM Physical Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2020

Characterization of the 2008 Phreatomagmatic Eruption of Okmok From ArcticDEM and InSAR: Deposition, Erosion, and Deformation

Okmok volcano, one of the most active volcanoes in the Aleutian Islands, erupted between 12 July and 19 August 2008 and produced a new tephra cone (Ahmanilix) up to 253 m high. A coeruptive deposit thickness map and the posteruptive elevation change rate map are generated for this phreatomagmatic eruption using the new, high-resolution (2 m) digital elevation model (DEM) time series provided by ArcticDEM. The total dense rock equivalent (DRE) volume over the proximal deposit field classified from the elevation change map is estimated as 0.147 ± 0.001 km 3 DRE (bulk volume of 0.453 ± 0.003 km 3 ). Combined with the distal deposit volume derived from field measurements, the total DRE is 0.25 km 3 (bulk volume of 0.76 km 3 ). The estimated erosion rate is up to −15 ± 3 m/year along the flanks of Ahmanilix, and the redeposition rate is about 1 ± 0.3 m/year at the base and 6 ± 1 m/year within the center of Ahmanilix. The surface elevation change also reveals a large ground surface collapse that disrupted the old Cone D lava bench, with a total collapse area of 0.15 km2 and a volume of (7.1 ± 0.4) × 10−3 km 3 . We also produce a coeruptive deformation map from satellite interferometric synthetic aperture radar (InSAR) using topography from both ArcticDEM and the Shuttle Radar Topography Mission (SRTM) and compare the results. Finally, based on historical eruption volumes, the magma supply rate at Okmok is estimated as (1.77 ± 0.1) × 10−3 km 3 /year.

Chunli Dai↗
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

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRS1984

A modeling analysis program for the JPL table mountain Io sodium cloud

A data quality review for the entire set of the 1981 Region B/C images has been completed and is presented. The review indicates that almost all images are of sufficient quality to be valuable in our analysis of this data set. Five data-correlation studies for the same data set have also been completed and are useful in classifying and studying the sodium cloud morphology and its interactions with solar radiation pressure and the plasma torus. Additional progress in developing new image processing techniques and in improving the Io sodium cloud model is also discussed.

Smyth, W. H.↗
NASA NTRS2020

Deep Learning Models for Planetary Seismicity Detection

Research in planetary seismology is fundamentally constrained by a lack of data. Seismo-logical science products of future missions can typically only be informed by theoretical signal/noise characteristics of the environment or likely Earth-analogues. Although objectives can be re-assessed after some initial data-collection upon lander arrival, transfer of high-resolution data back to Earth is costly on lander power usage. Over the last several years, development of GPU computing techniques and open-source high-level APIs have led to rapid advances in deep learning within the fields of computer vision, natural language processing, and collaborative filtering. These techniques are actively being adapted in seismology for a variety of tasks, including: earthquake detection, seismic phase discrimination, and ground-motion prediction. Until the recent detection of mars quakes during the Mars InSight mission, the only other measurements of seismicity recorded outside of Earth was on the Moon during the Apollo missions between 1969 to 1977. These unique data sets have been periodically revisited using new seismological methods, including ambient noise interferometry and Hidden Markov Models. Our objective is to develop a deep learning seismic detector and use it to catalog moonquakes from the Apollo 17 Lunar Seismic Profiling Experiment (LSPE) and compare the results with those obtained by other methods. Additionally, we will assess the accuracy tradeoff between using a training set of lunar data and one composed of Earth seismicity. In this document, we present preliminary results using a prototype classifier trained on a small set of earthquakes that was able to obtain detections for LSPE moonquakes with a greater accuracy than a recent study using Hidden Markov Models.

Civilini, F.↗
NASA NTRS2017

An Evaluation of Teleconnections Over the United States in an Ensemble of AMIP Simulations with the MERRA-2 Configuration of the GEOS Atmospheric Model

The atmospheric general circulation model that is used in NASA's Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) is evaluated with respect to the relationship between large-scale teleconnection patterns and daily temperature and precipitation over the United States (US) using a ten-member ensemble of simulations, referred to as M2AMIP. A focus is placed on four teleconnection patterns that are known to influence weather and climate in the US: El Nino Southern Oscillation, the Pacific Decadal Oscillation, the North Atlantic Oscillation, and the Pacific-North American Pattern. The monthly and seasonal indices associated with the patterns are correlated with daily temperature and precipitation statistics including: (i) monthly mean 2 m temperature and precipitation, (ii) the frequency of extreme temperature events at the 90th, 95th, and 99th percentiles, and (iii) the frequency and intensity of extreme precipitation events classified at the 90th, 95th, and 99th percentiles.Correlations obtained with M2AMIP data and thus the strength of teleconnections in the free-running model are evaluated through comparison against corresponding correlations computed from observations and from MERRA-2. Overall, the strongest teleconnections in all datasets occur during the winter and coincide with the largest agreement between the observations, MERRA-2, and M2AMIP. When M2AMIP does capture the correlation seen in observations, there is a tendency for the spatial extent to be exaggerated. The weakest agreement between the data sources, for all teleconnection patterns, is in the correlation with extreme precipitation; however there are discrepancies between the datasets in the number of days with at least 1 mm of precipitation: M2AMIP has too few days with precipitation in the Northwest and the Northern Great Plains and too many days in the Northeast. In JJA, M2AMIP has too few days with precipitation in the western two-thirds of the country and too many days with precipitation along the east coast.

JJA↗
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 NTRS1976

Results of Large Area Crop Inventory Experiment (LACIE) drought analysis (South Dakota drought 1976)

LACIE using techniques developed from the southern Great Plains drought analysis indicated the potential for drought damage in South Dakota. This potential was monitored and as it became apparent that a drought was developing, LACIE implemented some of the procedures used in the southern Great Plains drought. The technical approach used in South Dakota involved the normal use of LACIE sample segments (5 x 6 nm) every 18 days. Full frame color transparencies (100 x 100 nm) were used on 9 day intervals to identify the drought area and to track overtime. The green index number (GIN) developed using the Kauth transformation was computed for all South Dakota segments and selected North Dakota segments. A scheme for classifying segments as drought affected or not affected was devised and tested on all available 1976 South Dakota data. Yield model simulations were run for all CRD's Crop Reporting District) in South Dakota.

Thompson, D. R.↗