Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “pattern 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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Discriminating coastal rangeland production and improvements with computer aided techniques

The feasibility and utility of using satellite data and computer-aided remote sensing analysis techniques to conduct range inventories were tested. This pilot study was focused over a 250,000 acre site in Galveston and Brazoria Counties along the Texas Gulf Coast. Rectified enlarged aircraft color infrared photographs of this site were used as the ground truth base. The different land categories were identified, delineated, and measured. Multispectral scanner (MSS) bulk data from LANDSAT-1 was received and analyzed with the Image 100 pattern recognition system. Features of interest were delineated on the image console giving the number of picture elements classified; the picture elements were converted to acreages and the accuracy of the technique was evaluated by comparison with data base results for three test sites. The accuracies for computer aided classification of coastal marshes ranged from 89% to 96%.

Reeves, C. A.↗
NASA NTRS1986

An intelligent object recognizer and classification system for astronomical use

An account is given of an image-processing system based on AI concepts, which allows input images produced by the CCT/Transit Instrument to be compared with a standard-object hierarchylike network of prototypes presented within the computer as 'frames'. Each frame contains information concerning either a standard object or the links among such objects. This method, by comparison to conventional, statistically-based pattern recognition systems, classifies data as an astronomer would and thereby lends credibility to its conclusions; it also furnishes a natural avenue for the machine's serendipitous discovery of new classes of objects.

Bernat, Andrew P.↗
NASA NTRS2016

Reanalyzing Environmental Lidar Data for Archaeology: Mesoamerican Applications and Implications

This paper presents a preliminary archaeological assessment of extensive transects of lidar recently collected by environmental scientists over southern Mexico using the G-LiHT system of NASA's Goddard Space Flight Center. In particular, this article offers the results of a first phase of research, consisting of: 1) characterization and classification of the cultural and ecological context of the samples, and 2) bare earth processing and visual inspection of a sample of the flight paths for identification of probable anthropogenic Precolumbian features. These initial results demonstrate that significant contributions to understanding variations in Precolumbian land-use and settlement patterns and change is possible with truly multi-regional lidar surveys not originally captured for archaeological prospection. We point to future directions for the development of archaeological applications of this robust data set. Finally, we offer the potential for enriching archaeological research through tightly coupled collaborations with environmental science and monitoring. Archaeologists in the neotropics can acquire more data, better realize the full potential of lidar surveys, and better contribute to interdisciplinary studies of human-environmental dynamic systems through regionally focused and collaborative scientific research.

Golden, Charles↗
NASA NTRS1982

Different atmospheric effects in remote sensing of uniform and nonuniform surfaces

The atmospheric effect on the radiance of sunlight scattered from the earth-atmosphere system is greatly dependent on the surface reflectance pattern, the contrast between adjacent fields, and the optical properties of the atmosphere. In addition, the atmospheric effect is described by the range and magnitude of the adjacency effects, the atmospheric modulation transfer function, and the apparent spatial resolution of remotely sensed imagery. This paper discusses the atmospheric effect on classification of surface features and shows that surface nonuniformity can be used for developing procedures to remove the atmospheric effect from the satellite imagery.

Kaufman, Y. J.↗
NASA NTRS1976

Digital preprocessing and classification of multispectral earth observation data

The development of airborne and satellite multispectral image scanning sensors has generated wide-spread interest in application of these sensors to earth resource mapping. These point scanning sensors permit scenes to be imaged in a large number of electromagnetic energy bands between .3 and 15 micrometers. The energy sensed in each band can be used as a feature in a computer based multi-dimensional pattern recognition process to aid in interpreting the nature of elements in the scene. Images from each band can also be interpreted visually. Visual interpretation of five or ten multispectral images simultaneously becomes impractical especially as area studied increases; hence, great emphasis has been placed on machine (computer) techniques for aiding in the interpretation process. This paper describes a computer software system concept called LARSYS for analysis of multivariate image data and presents some examples of its application.

Anuta, P. E.↗
NASA NTRS2021

Linear and Nonlinear Trend Analyzes in Global Satellite‐Based Precipitation, 1998–2017

Precipitation varies spatio‐temporally in amount, intensity, and frequency. Although, much research has been conducted on analyzing precipitation patterns and variation at the global scale, trend types have still not received much attention. This study developed a new polynomial‐based model for detecting nonlinear and linear trends in a satellite precipitation product (TRMM 3B43) for the 1998–2017 period at a near‐global scale. We used an automated trend classification method that detects significant trends and classifies them into linear and nonlinear (cubic, quadratic, and concealed) trend types in satellite‐based precipitation at near‐global, continental, and climate zone scales. We found that 12.3% of pixel‐based precipitation time series across the globe have significant trend at 0.05 significance level (50% positive and 50% negative trends). In all continents except Asia, decreasing trends were found to cover larger areas than corresponding increasing trends. Regarding climate zone and precipitation trend change, our results indicate that a linear trend is dominant in the warm temperate (77.7%) and equatorial climates (80.4%) while the least linear change was detected in the polar climate (68.9%). The combined results of continental and climate zone scales indicate significant increasing trends in Asia and arid climate over the last 20 yr. Furthermore, positive trends were found to be more significant at the continental scale, particularly, in Asia relative to the climate zone scale. Linear change in precipitation (80%) was the most dominant trend observed as opposed to nonlinear (quadratic [11%] and cubic [9%]) trend types at the global scale.

Majid Kazemzadeh↗
NASA NTRS1973

A preliminary investigation of bird classification by Doppler radar

A preliminary study of the application of Doppler radar to the classification of birds is reported. The desirability for improvements in bird classification stems primarily from the hazards they present to jet aircraft in flight and in the vicinity of airports. A secondary need exists in the study of bird migration. The wing body and tail motion of a bird in flight reflect signals which, when analyzed properly present a signature of wing beat pattern which is unique for each bird species. Although the results of this investigation did not validate the feasibility of classifying bird species, they do indicate that a more thorough investigation is warranted. Certain gross characteristics such as wing beat rates, multiple bird patterns, and bird maneuverability, were indicated clearly in the results. Large birds with slow wing beat rates appear to be the most optimum subject for further study with the X-band Doppler radar used in this investigation.

Martinson, L. W.↗
NASA NTRS1976

Evaluation of algorithms for estimating wheat acreage from multispectral scanner data

The author has identified the following significant results. Fourteen different classification algorithms were tested for their ability to estimate the proportion of wheat in an area. For some algorithms, accuracy of classification in field centers was observed. The data base consisted of ground truth and LANDSAT data from 55 sections (1 x 1 mile) from five LACIE intensive test sites in Kansas and Texas. Signatures obtained from training fields selected at random from the ground truth were generally representative of the data distribution patterns. LIMMIX, an algorithm that chooses a pure signature when the data point is close enough to a signature mean and otherwise chooses the best mixture of a pair of signatures, reduced the average absolute error to 6.1% and the bias to 1.0%. QRULE run with a null test achieved a similar reduction.

Nalepka, R. F.↗
NASA NTRS1973

Agricultural land use mapping

The author has identified the following significant results. Agricultural areas were selected or analysis in southeastern Pennsylvania, north central Montana, and southern Texas. These three sites represent a broad range of soils, soil parent materials, climate, modes of agricultural operation, crops, and field sizes. In each of these three sites, ERTS-1 digital data were processed to determine the feasibility of automatically mapping agricultural land use. In Pennsylvania, forest land, cultivated land, and water were separable within a 25,000 acre area. Four classes of water were also classified and identified, using ground truth. A less complex land use pattern was analyzed in Hill County, Montana. A land use map was prepared shown alternating patterns of summer fallow and stubble fields. The location of farmsteads could be inferred, along with that of a railroad line. A river and a creek flowing into the river were discernible. Six categories of water, related to sediment content and depth, were defined in the reservoir held by the Fresno dam. These classifications were completed on a 150 square mile area. Analysis of the data from Texas is in its formative stages. A test site has been selected and a brightness map has been produced.

Mcmurtry, G. J.↗
NASA NTRS1982

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Hoffer, R. M.↗
NASA NTRS2005

Chapter 19A – Radar Cross Section

The material in this Section will provide the novice Flight Test Engineer (FTE) with a brief overview of the radar cross section (RCS) concept. Brief details of radar range descriptions, operations, target calibrations, and data reduction are included. It is noted that these range operations concentrate on the "static" test range rather than the much more sophisticated "dynamic" test range. The dynamic test range is what the FTE will probably work with. Radar reflectivity measurement has developed over the last two decades from a relatively simple endeavor involving the measurement of target RCS amplitude statistics to involving wide band, coherent systems that can measure high resolution images of targets as well as, in many cases, the polarization and phasing properties. This rapid growth in RCS technology has occurred because of the increased use of radar in today's commercial and military systems. In general, the goal for commercial systems is to enhance radar reflectivity, whereas the military goal is to reduce radar reflectivity. Also, classification and identification are important for military purposes because in adverse weather radar may be the only system that may be capable of separating enemy targets from friendly ones. This Section will discuss the fundamentals of RCS. Since the flight test techniques associated with RCS measurements are very similar to those of antenna pattern measurements, both will be presented at the same time in Section 19B Antenna Radiation Pattern Measurements.

Robert W Borek↗
NASA NTRS1988

Cloud field classification based upon high spatial resolution textural features. I - Gray level co-occurrence matrix approach

Stratocumulus, cumulus, and cirrus clouds were identified on the basis of cloud textural features which were derived from a single high-resolution Landsat MSS NIR channel using a stepwise linear discriminant analysis. It is shown that, using this method, it is possible to distinguish high cirrus clouds from low clouds with high accuracy on the basis of spatial brightness patterns. The largest probability of misclassification is associated with confusion between the stratocumulus breakup regions and the fair-weather cumulus.

Welch, R. M.↗
NASA NTRS1993

Automated cloud classification with a fuzzy logic expert system

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Tovinkere, Vasanth↗
NASA NTRS1993

Ten years in the library: new data confirm paleontological patterns

A comparison is made between compilations of times of origination and extinction of fossil marine animal families published in 1982 and 1992. As a result of ten years of library research, half of the information in the compendia has changed: families have been added and deleted, low-resolution stratigraphic data been improved, and intervals of origination and extinction have been altered. Despite these changes, apparent macroevolutionary patterns for the entire marine fauna have remained constant. Diversity curves compiled from the two data bases are very similar, with a goodness-of-fit of 99%; the principal difference is that the 1992 curve averages 13% higher than the older curve. Both numbers and percentages of origination and extinction also match well, with fits ranging from 83% to 95%. All major events of radiation and extinction are identical. Therefore, errors in large paleontological data bases and arbitrariness of included taxa are not necessarily impediments to the analysis of pattern in the fossil record, so long as the data are sufficiently numerous.

NASA Discipline Number 52-40↗
NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗
NASA NTRS2003

Enhancing and Archiving the APS Catalog of the POSS I

We have worked on two different projects: 1) Archiving the APS Catalog of the POSS I for distribution to NASA's NED at IPAC, SIMBAD in France, and individual astronomers and 2) The automated morphological classification of galaxies. We have completed archiving the Catalog into easily readable binary files. The database together with the software to read it has been distributed on DVD's to the national and international data centers and to individual astronomers. The archived Catalog contains more than 89 million objects in 632 fields in the first epoch Palomar Observatory Sky Survey. Additional image parameters not available in the original on-line version are also included in the archived version. The archived Catalog is also available and can be queried at the APS web site (URL: http://aps.umn.edu) which has been improved with a much faster and more efficient querying system. The Catalog can be downloaded as binary datafiles with the source code for reading it. It is also being integrated into the SkyQuery system which includes the Sloan Digital Sky Survey, 2MASS, and the FIRST radio sky survey. We experimented with different classification algorithms to automate the morphological classification of galaxies. This is an especially difficult problem because there are not only a large number of attributes or parameters and measurement uncertainties, but also the added complication of human disagreement about the adopted types. To solve this problem we used 837 galaxy images from nine POSS I fields at the North Galactic Pole classified by two independent astronomers for which they agree on the morphological types. The initial goal was to separate the galaxies into the three broad classes relevant to issues of large scale structure and galaxy formation and evolution: early (ellipticals and lenticulars), spirals, and late (irregulars) with an accuracy or success rate that rivals the best astronomer classifiers. We also needed to identify a set of parameters derived from the digitized images that separate the galaxies by type. The human eye can easily recognize complicated patterns in images such as spiral arms which can be spotty, blotchy affairs that are difficult for automated techniques. A galaxy image can potentially be described by hundreds of parameters, all of which may have some relation to the morphological type. In the set of initial experiments we used 624 such parameters, in two colors, blue and red. These parameters include the surface brightness and color measured at different radii, ratios of these parameters at different radii, concentration indices, Fourier transforms and wavelet decomposition coefficients. We experimented with three different classes of classification algorithms; decision trees, k-nearest neighbors, and support vector machines (SVM). A range of experiments were conducted and we eventually narrowed the parameters to 23 selected parameters. SVM consistently outperformed the other algorithms with both sets of features. By combining the results from the different algorithms in a weighted scheme we achieved an overall classification success of 86%.

Humphreys, Roberta M.↗
NASA NTRS2020

Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data

East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

Sheng-Hsiang Wang↗