Search results for "method"

showing 10 items of 13253 documents

Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses

2020

The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to hand…

010504 meteorology & atmospheric sciencesComputer scienceUAVReal-time computingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION0211 other engineering and technologiesComputerApplications_COMPUTERSINOTHERSYSTEMS77 GHz02 engineering and technologylcsh:Chemical technology01 natural sciencesBiochemistryArticleAnalytical Chemistrylaw.inventionARS-408lawlcsh:TP1-1185ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMSElectrical and Electronic EngineeringRadarInstrumentationARS-404021101 geological & geomatics engineering0105 earth and related environmental sciencesRadarAtomic and Molecular Physics and OpticsEarth surfaceAutomotive radarKey (cryptography)Sensors
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FAME: Software for analysing rock microstructures

2016

Determination of rock microstructures leads to a better understanding of the formation and deformation of polycrystalline solids. Here, we present FAME (Fabric Analyser based Microstructure Evaluation), an easy-to-use MATLAB®-based software for processing datasets recorded by an automated fabric analyser microscope. FAME is provided as a MATLAB®-independent Windows® executable with an intuitive graphical user interface. Raw data from the fabric analyser microscope can be automatically loaded, filtered and cropped before analysis. Accurate and efficient rock microstructure analysis is based on an advanced user-controlled grain labelling algorithm. The preview and testing environments simplif…

010504 meteorology & atmospheric sciencesComputer sciencebusiness.industryOrientation (computer vision)AnalyserComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputer.file_format010502 geochemistry & geophysics01 natural sciencesVisualizationSoftwareComputer graphics (images)Batch processingExecutableComputers in Earth SciencesbusinesscomputerSimulation0105 earth and related environmental sciencesInformation SystemsRock microstructureGraphical user interfaceComputers & Geosciences
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Stochastic Galerkin method for cloud simulation

2018

AbstractWe develop a stochastic Galerkin method for a coupled Navier-Stokes-cloud system that models dynamics of warm clouds. Our goal is to explicitly describe the evolution of uncertainties that arise due to unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximation in both space and time and exponential convergence in the stochastic space. Our numerical results…

010504 meteorology & atmospheric sciencesComputer scienceuncertainty quantificationQC1-999cloud dynamicsFOS: Physical sciencesCloud simulation65m15010103 numerical & computational mathematics01 natural sciencespattern formationMeteorology. ClimatologyFOS: MathematicsApplied mathematicsMathematics - Numerical Analysis0101 mathematicsStochastic galerkin0105 earth and related environmental sciencesnavier-stokes equationsPhysics65m2565l05Numerical Analysis (math.NA)65m06Computational Physics (physics.comp-ph)stochastic galerkin method35l4535l65finite volume schemesQC851-999Physics - Computational Physicsimex time discretization
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SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information

2018

Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately.

010504 meteorology & atmospheric sciencesContextual image classificationComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION0211 other engineering and technologiesHigh resolutionPattern recognition02 engineering and technologySpace (commercial competition)01 natural sciencesSupport vector machineSatelliteArtificial intelligencebusiness021101 geological & geomatics engineering0105 earth and related environmental sciencesProceedings of the 12th International Conference on Intelligent Systems: Theories and Applications
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SAR Image Classification Combining Structural and Statistical Methods

2011

The main objective of this paper is to develop a new technique of SAR image classification. This technique combines structural parameters, including the Sill, the slope, the fractal dimension and the range, with statistical methods in a supervised image classification. Thanks to the range parameter, we define the suitable size of the image window used in the proposed approach of supervised image classification. This approach is based on a new way of characterising different classes identified on the image. The first step consists in determining relevant area of interest. The second step consists in characterising each area identified, by a matrix. The last step consists in automating the pr…

010504 meteorology & atmospheric sciencesContextual image classificationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONProcess (computing)Pattern recognition02 engineering and technology01 natural sciencesFractal dimensionImage (mathematics)Range (mathematics)Matrix (mathematics)Fractal[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceVariogrambusinessComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesMathematics
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The size, shape, density and ring of the dwarf planet Haumea from a stellar occultation

2017

Ortiz, José Luis et. al.

010504 meteorology & atmospheric sciencesEuropean communityTrans Neptunian ObjectDwarf planetHaumeaFOS: Physical sciencesLibrary scienceshape01 natural sciencessizedwarf planetNeptuneFísica Aplicada0103 physical sciencesHaumeamedia_common.cataloged_instanceEuropean unionInstrumentation and Methods for Astrophysics (astro-ph.IM)010303 astronomy & astrophysicsComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciencesmedia_commonEarth and Planetary Astrophysics (astro-ph.EP)Physics[PHYS]Physics [physics]density2003 EL61 ; Kuiper-belt ; photometric-observations ; collisional family ; object ; bodies ; albedo ; satellites ; UranusDwarf planetsMultidisciplinaryEuropean researchAsteroidTrans-NeptunianAstronomyStellar occultationMoons of HaumeaStellar occultationstellar occultationAstrophysics::Earth and Planetary AstrophysicsAstrophysics - Instrumentation and Methods for Astrophysics[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]ringAstrophysics - Earth and Planetary Astrophysics
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Slender Ca II H fibrils mapping magnetic fields in the low solar chromosphere

2017

S. Jafarzadeh et. al.

010504 meteorology & atmospheric sciencesExtrapolationFOS: Physical scienceschromosphere [Sun]Field strengthAstrophysicsDense forest01 natural sciencesMethods: observational0103 physical sciencesSunriseAstrophysics::Solar and Stellar Astrophysicsobservational [Methods]010303 astronomy & astrophysicsChromosphereSun: magnetic fieldsSolar and Stellar Astrophysics (astro-ph.SR)0105 earth and related environmental sciencesPhysicsSolar observatorySun: chromosphereAstronomy and AstrophysicsMagnetic fieldmagnetic fields [Sun]Astrophysics - Solar and Stellar AstrophysicsSpace and Planetary SciencePhysics::Space PhysicsMagnetohydrodynamics
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Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

2019

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …

010504 meteorology & atmospheric sciencesFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTRE0208 environmental biotechnologySoil ScienceReview02 engineering and technologyPhotochemical Reflectance Index01 natural sciencesArticleGEO/11 - GEOFISICA APPLICATASIF retrieval methodsRadiative transfer modellingRadiative transfer910 Geography & travelComputers in Earth SciencesChlorophyll fluorescence1111 Soil Science1907 GeologyAirborne instruments0105 earth and related environmental sciencesRemote sensingStress detectionGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERA1903 Computers in Earth SciencesPrimary productionGeologyVegetationPassive optical techniquesField (geography)020801 environmental engineeringGEO/10 - GEOFISICA DELLA TERRA SOLIDA10122 Institute of GeographySun-induced fluorescenceRemote sensing (archaeology)Sun-induced fluorescence Steady-state photosynthesis Stress detection Radiative transfer modelling SIF retrieval methods. Satellite sensors Airborne instruments Applications Terrestrial vegetation Passive optical techniques. ReviewApplicationsTerrestrial vegetationEnvironmental scienceSatelliteSteady-state photosynthesisSatellite sensors
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Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

2015

In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…

010504 meteorology & atmospheric sciencesFeature extraction0211 other engineering and technologiesRelative spectral alignment02 engineering and technology3107 Atomic and Molecular Physics and Optics01 natural sciencesCross-sensorCanonical correlation analysis1706 Computer Science Applications910 Geography & travelComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsGround truthbusiness.industry1903 Computers in Earth SciencesKernel methodsPattern recognitionReal imageAtomic and Molecular Physics and OpticsComputer Science Applications10122 Institute of GeographyTransformation (function)Kernel methodChange detectionFeature extraction2201 Engineering (miscellaneous)Artificial intelligencebusinessCanonical correlationChange detectionCurse of dimensionalityISPRS Journal of Photogrammetry and Remote Sensing
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Astrometric detection of a low-mass companion orbiting the star AB Doradus

1997

International audience; We report submilliarcsecond-precise astrometric measurements for the late-type star AB Doradus via a combination of VLBI (very long baseline interferometry) and HIPPARCOS data. Our astrometric analysis results in the precise determination of the kinematics of this star, which reveals an orbital motion readily explained as caused by gravitational interaction with a low-mass companion. From the portion of the reÑex orbit covered by our data and using a revised mass of the primary star (0.76 M _) derived from our new value of the parallax (66.3 mas \ n \ 67.2 mas), we Ðnd the dynamical mass of the newly discovered companion to be between 0.08 and 0.11 If accurate photom…

010504 meteorology & atmospheric sciencesGalactic astronomyStellar massBrown dwarfAstrophysicsAstrophysics::Cosmology and Extragalactic Astrophysics01 natural sciencesstars: low-mass0103 physical sciencesVery-long-baseline interferometryAstrophysics::Solar and Stellar Astrophysics010303 astronomy & astrophysicsAstrophysics::Galaxy Astrophysics0105 earth and related environmental sciencesPhysicsstars: individual (AB Doradus)[SDU.ASTR.SR]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]Astrophysics::Instrumentation and Methods for AstrophysicsAstronomyAstronomy and AstrophysicsAstrometryInterferometrySpace and Planetary Sciencetechniques: interferometricOrbital motionastrometryAstrophysics::Earth and Planetary Astrophysics[SDU.ASTR.GA]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA]stars: kinematicsLow Massbrown dwarfs
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