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At least 271 records · Page 15
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NASA NTRS1992

Character recognition using a neural network model with fuzzy representation

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Tavakoli, Nassrin↗
NASA NTRSDate not supplied

Cloud-Based Solutions for Monitoring Coastal Ecosystems and Prioritization of Restoration Efforts Across Belize

In recent years the availability of automated change detection algorithms in Google Earth Engine has permitted cloud-based processing of large time series of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EO) , leveraging the Landsat archive and data from other sensors to detect disturbance in forest ecosystems. Despite the wide adoption of these methods, robust documentation and growing community of users, little research has explored their use in mangrove environments. Mangroves are dynamic environments subject to changes due to not only the natural migration of mudflats but also coastal erosion, urban expansion, aquaculture practices, etc. This work aims to identify best practices for the application of these models to identify and monitor changes in the Belizean mangroves, which experienced an estimated 5.4% decrease in extent between 1980 and 2017 (Cherrington et al. 2020). Partnering directly with the Belizean Forest Department, our team will develop a replicable, efficient methodology to annually update the country’s mangrove extent employing EO-based change detection. This collaboration supports Belize’s marine conservation and climate resilience efforts, such as its Blue Bond agreement with the Nature Conservancy, building on national processes to monitor these critical ecosystems.

Mangroves↗
NASA NTRS1983

A stochastic atmospheric model for remote sensing applications

There are many factors which reduce the accuracy of classification of objects in the satellite remote sensing of Earth's surface. One important factor is the variability in the scattering and absorptive properties of the atmospheric components such as particulates and the variable gases. For multispectral remote sensing of the Earth's surface in the visible and infrared parts of the spectrum the atmospheric particulates are a major source of variability in the received signal. It is difficult to design a sensor which will determine the unknown atmospheric components by remote sensing methods, at least to the accuracy needed for multispectral classification. The problem of spatial and temporal variations in the atmospheric quantities which can affect the measured radiances are examined. A method based upon the stochastic nature of the atmospheric components was developed, and, using actual data the statistical parameters needed for inclusion into a radiometric model was generated. Methods are then described for an improved correction of radiances. These algorithms will then result in a more accurate and consistent classification procedure.

Turner, R. E.↗
NASA NTRS1992

Object linking in repositories

This topic is covered in three sections. The first section explores some of the architectural ramifications of extending the Eichmann/Atkins lattice-based classification scheme to encompass the assets of the full life cycle of software development. A model is considered that provides explicit links between objects in addition to the edges connecting classification vertices in the standard lattice. The second section gives a description of the efforts to implement the repository architecture using a commercially available object-oriented database management system. Some of the features of this implementation are described, and some of the next steps to be taken to produce a working prototype of the repository are pointed out. In the final section, it is argued that design and instantiation of reusable components have competing criteria (design-for-reuse strives for generality, design-with-reuse strives for specificity) and that providing mechanisms for each can be complementary rather than antagonistic. In particular, it is demonstrated how program slicing techniques can be applied to customization of reusable components.

Eichmann, David↗
NASA NTRS2017

Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Classification; Climate change; Crop model; Ensemb↗
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS1984

IUE observations of the clusters of the Magellanic Clouds

IUE observations of 17 clusters in the Large Magellanic Cloud are presented. The derived ultraviolet colors become progressively redder as the cluster's classification in the scheme of Searle, Wilkinson, and Bagnuolo increases. With the aid of theoretical models of the integrated light of coeval clusters of varying ages, it is shown that the SWB classification is one of increasing age accompanied by decreasing metallicity toward later SWB classes. The behavior of the UV absorption line features in the integrated light of the LMC clusters supports the contention.

Cohen, J. G.↗
NASA NTRS1982

Evaluation of land cover change detection techniques using Landsat MSS data

Four techniques for detecting land cover change with Landsat MSS data were evaluated for a test site in east central Louisiana where forestland is being converted to cropland and pasture. The four techniques were: (1) post-classification differencing, (2) composite classification, (3) radiance value shift, and (4) regression modeling. Three of the techniques gave acceptable accuracies. The main differences were the amount of computer time and the level of complexity.

Burns, G. S.↗
NASA NTRS1989

The evaluative imaging of mental models - Visual representations of complexity

The paper deals with some design issues involved in building a system that could visually represent the semantic structures of training materials and their underlying mental models. In particular, hypermedia-based semantic networks that instantiate classification problem solving strategies are thought to be a useful formalism for such representations; the complexity of these web structures can be best managed through visual depictions. It is also noted that a useful approach to implement in these hypermedia models would be some metrics of conceptual distance.

Dede, Christopher↗
NASA NTRS1990

Studies in knowledge-based diagnosis of failures in robotic assembly

The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

Lam, Raymond K.↗
NASA NTRS1978

Multispectral scanner system parameter study and analysis software system description, volume 2

The author has identified the following significant results. The integration of the available methods provided the analyst with the unified scanner analysis package (USAP), the flexibility and versatility of which was superior to many previous integrated techniques. The USAP consisted of three main subsystems; (1) a spatial path, (2) a spectral path, and (3) a set of analytic classification accuracy estimators which evaluated the system performance. The spatial path consisted of satellite and/or aircraft data, data correlation analyzer, scanner IFOV, and random noise model. The output of the spatial path was fed into the analytic classification and accuracy predictor. The spectral path consisted of laboratory and/or field spectral data, EXOSYS data retrieval, optimum spectral function calculation, data transformation, and statistics calculation. The output of the spectral path was fended into the stratified posterior performance estimator.

Landgrebe, D. A.↗
NASA NTRS1981

Earth feature identification for onboard multispectral data editing: Computational experiments

A computational model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing smart sensors which can process, edit, and classify the data that they acquire. An evaluation of sensor system performance and design tradeoffs involves classification rates and errors as a function of number and location of spectral channels, radiometric sensitivity and calibration accuracy, target discrimination assignments, and accuracy and frequency of compensation for imaging conditions. This model provides a link between the radiometric and statistical properties of the signals to be classified and the performance characteristics of electro-optical sensors and data processing devices. Preliminary computational results are presented which illustrate the editing performance of several remote sensing approaches.

Aherron, R. M.↗
NASA NTRS2012

The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

Ancel, Ersin↗
NASA NTRS2019

The Badhwar-O'Neill 2020 Model

The Badhwar-O'Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within ±15% of a large fraction of the available measurements (26,269 of 27,646 → 95%).

Slaba, Tony C.↗
NASA NTRS2020

The Badhwar-O'Neill 2020 Model

The Badhwar-O’Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high-precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within +15% of a large fraction of the available measurements (26,269 of 27,646  95%.

T C Slaba↗
NASA NTRS2005

Spectral Ratio Imaging with Hyperion Satellite Data for Geological Mapping

Since the advent of LANDSAT I in 1972, many different multispectral satellites have been orbited by the U.S. and other countries. These satellites have varied from 4 spectral bands in LANDSAT I to 14 spectral bands in the ASTER sensor aboard the TERRA space platform. Hyperion is a relatively new hyperspectral sensor with over 220 spectral bands. The huge increase in the number of spectral bands offers a substantial challenge to computers and analysts alike when it comes to the task of mapping features on the basis of chemical composition, especially if little or no ground truth is available beforehand from the area being mapped. One approach is the theoretical approach of the modeler, where all extraneous information (atmospheric attenuation, sensor electronic gain and offset, etc.) is subtracted off and divided out, and laboratory (or field) spectra of materials are used as training sets to map features in the scene of similar composition. This approach is very difficult to keep accurate because of variations in the atmosphere, solar illumination, and sensor electronic gain and offset that are not always perfectly recorded or accounted for. For instance, to apply laboratory or field spectra of materials as data sets from the theoretical approach, the header information of the files must reflect the correct, up-to-date sensor electronic gain and offset and the analyst must pick the exact atmospheric model that is appropriate for the day of data collection in order for classification procedures to accurately match pixels in the scene with the laboratory or field spectrum of a desired target on the basis of the hyperspectral data. The modeling process is so complex that it is difficult to tell when it is operating well or determine how to fix it when it is incorrect. Recently RSI has announced that the latest version of their ENVI software package is not performing atmospheric corrections correctly with the FLAASH atmospheric model. It took a long time to determine that it was wrong, and may take an equally long time (or longer) to fix.

Vincent, Robert K.↗
NASA NTRS2002

Machine Learning for Biological Trajectory Classification Applications

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Sbalzarini, Ivo F.↗