Search results for "TC1501-1800"

showing 6 items of 6 documents

Traitement de données RGB et Lidar à extrêmement haute résolution: retombées de la compétition de fusion de données 2015 de l'IEEE GRSS - Partie A / …

2016

International audience; In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the sci…

Atmospheric Science010504 meteorology & atmospheric sciencesComputer scienceMULTIMODAL-DATA FUSIONGeophysics. Cosmic physics0211 other engineering and technologies02 engineering and technologyCONTESTcomputer.software_genre01 natural sciencesOutcome (game theory)LIDARTraitement des imagesIMAGE ANALYSIS AND DATA FUSION (IADF)DEEP NEURAL NETWORKSDeep neural networksTraitement du signal et de l'imageMULTIRESOLUTION910 Geography & travelMultiresolutionGround truthLANDCOVER CLASSIFICATIONIMAGE AERIENNE1903 Computers in Earth SciencesBenchmarkingVision par ordinateur et reconnaissance de formesOcean engineering10122 Institute of GeographyLidarDeep neural networksData miningExtremely high spatial resolutionMultimodal-data fusionLiDARComputers in Earth Sciences; Atmospheric ScienceImage analysis and data fusion (IADF)EXTREMELY HIGH SPATIAL RESOLUTIONCLASSIFICATIONTRAITEMENT IMAGE1902 Atmospheric ScienceAPPRENTISSAGE STATISTIQUEComputers in Earth SciencesTELEDETECTIONSynthèse d'image et réalité virtuelleTC1501-1800021101 geological & geomatics engineering0105 earth and related environmental sciencesLandcover classificationmultiresolution-[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]QC801-809Intelligence artificielleMULTISOURCESensor fusionRGB color modelcomputerMultisource
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Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions

2021

The estimation of biophysical variables from remote sensing data raises important challenges in terms of the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains the spatial resolution while significantly increasing the subpixel land-cover heterogeneity. Precisely, this spatial variability often makes that rather different canopy structures are aggregated together, which eventually generates important deviations in the corresponding parameter quantification. In the context of the Copernicus program (and other related Earth Explorer missions), this article propose…

Atmospheric Science010504 meteorology & atmospheric sciencesComputer sciencevegetation mappingGeophysics. Cosmic physics0211 other engineering and technologiesContext (language use)02 engineering and technologyLand coverearthSentinel-2 (S2)01 natural sciencessentinel-3 (S3)FLEXcharacterizationComputers in Earth SciencesImage resolutionTC1501-1800spatial resolutionBiophysical productsSentinel-3 (S3)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingQC801-809biophysical productsbiological system modelingSubpixel renderingSpatial heterogeneityOcean engineeringinstrumentsfluorescence EXplorer (FLEX)Spatial ecologyflexible printed circuitssentinel-2 (S2)Spatial variabilityspatial distributionssensor phenomena
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The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina

2021

In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by the Argentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at on…

Atmospheric ScienceIndex (economics)Moisturebusiness.industryQC801-809Agricultural drought detectionGeophysics. Cosmic physicsArgentinastandardized precipitation evapotranspiration index (SPEI)standardized soil moisture anomalies (SSMA)Ocean engineeringAgricultureEvapotranspirationStatisticsEnvironmental scienceFalse positive ratePrecipitationComputers in Earth Sciencessoil moisture agricultural drought index (SMADI)Temporal scalesbusinessWater contentstandardized precipitation index (SPI)TC1501-1800IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Multitemporal Mosaicing for Sentinel-3/FLEX Derived Level-2 Product Composites

2020

The increasing availability of remote sensing data raises important challenges in terms of operational data provision and spatial coverage for conducting global studies and analyses. In this regard, existing multitemporal mosaicing techniques are generally limited to producing spectral image composites without considering the particular features of higher-level biophysical and other derived products, such as those provided by the Sentinel-3 (S3) and Fluorescence Explorer (FLEX) tandem missions. To relieve these limitations, this article proposes a novel multitemporal mosaicing algorithm specially designed for operational S3-derived products and also studies its applicability within the FLEX…

Atmospheric ScienceSource code010504 meteorology & atmospheric sciencesComputer scienceproduct compositesmedia_common.quotation_subjectGeophysics. Cosmic physics0211 other engineering and technologiesContext (language use)Automatic processing02 engineering and technology01 natural sciencesmosaicingConsistency (database systems)Data acquisitionFLEXProduct (category theory)sentinel-3 (S3Computers in Earth SciencesComposite materialFluorescence explorer (FLEX)fluorescence explorer (FLEX)TC1501-1800Sentinel-3 (S3)021101 geological & geomatics engineering0105 earth and related environmental sciencesmedia_commonQC801-809openaccess dataOcean engineeringCompositingtime seriesopen-access dataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

2021

The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two…

FOS: Computer and information sciencesAtmospheric ScienceComputer Science - Machine LearningGenerative adversarial networks010504 meteorology & atmospheric sciencesComputer scienceRemote sensing applicationdomain adaptationGeophysics. Cosmic physics0211 other engineering and technologiesCloud computing02 engineering and technologycomputer.software_genre01 natural sciencesImage (mathematics)Data modelingMachine Learning (cs.LG)convolutional neural networksFOS: Electrical engineering electronic engineering information engineeringLandsat-8Computers in Earth SciencesAdaptation (computer science)TC1501-1800021101 geological & geomatics engineering0105 earth and related environmental sciencesbusiness.industryQC801-809Image and Video Processing (eess.IV)Electrical Engineering and Systems Science - Image and Video ProcessingOcean engineeringTransformation (function)cloud detectionSatelliteData miningProba-VTransfer of learningbusinesscomputer
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Stochastic ship roll motion via path integral method

2010

ABSTRACTThe response of ship roll oscillation under random ice impulsive loads modeled by Poisson arrival process is very important in studying the safety of ships navigation in cold regions. Under both external and parametric random excitations the evolution of the probability density function of roll motion is evaluated using the path integral (PI) approach. The PI method relies on the Chapman-Kolmogorov equation, which governs the response transition probability density functions at two close intervals of time. Once the response probability density function at an early close time is specified, its value at later close time can be evaluated. The PI method is first demonstrated via simple …

Path integrallcsh:Ocean engineeringRandom impulsive ice loadingOcean EngineeringProbability density functionResponse amplitude operatorPoisson distributionShip roll Random impulsive ice loading Poisson distributionsymbols.namesakelcsh:VM1-989Control theorylcsh:TC1501-1800Parametric random excitationChapman-Kolmogorov equationMathematicsParametric statisticsOscillationMathematical analysisDynamics (mechanics)lcsh:Naval architecture. Shipbuilding. Marine engineeringControl and Systems EngineeringPath integral formulationPoisson distributionsymbolsShip rollSettore ICAR/08 - Scienza Delle CostruzioniChapman–Kolmogorov equationInternational Journal of Naval Architecture and Ocean Engineering
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