Search results for "Spectral imagery"

showing 7 items of 7 documents

Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral image…

2020

AbstractAround 350 million tonnes of plastics are annually produced worldwide. A remarkable percentage of these products is dispersed in the environment, finally reaching and dispersed in the marine environment. Recent field surveys detected microplastics’ concentrations in the Mediterranean Sea. The most commonly polymers found were polyethylene, polypropylene and viscose, ethylene vinyl acetate and polystyrene. In general, the in-situ monitoring of microplastic pollution is difficult and time consuming. The main goals of this work were to spectrally characterize the most commonly polymers and to quantify their spectral separability that may allow to determine optimal band combinations for…

Settore BIO/07 - EcologiaPollutionMicroplastics010504 meteorology & atmospheric sciencesmedia_common.quotation_subjectMultispectral imageOptical spectroscopylcsh:Medicine010501 environmental sciences01 natural sciencesArticleEnvironmental impactMediterranean sealcsh:Science0105 earth and related environmental sciencesmedia_commonRemote sensingMultidisciplinarySpectral signaturelcsh:RSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaCharacterization (materials science)Spectroradiometerspectroradiometric characterization sea plastic litters multispectral imageryEnvironmental sciencelcsh:QSatelliteSettore ICAR/06 - Topografia E CartografiaScientific Reports
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Remote sensing image segmentation by active queries

2012

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…

Active learningComputer scienceActive learning (machine learning)SvmMultispectral image0211 other engineering and technologies02 engineering and technologyMultispectral imageryClusteringMultispectral pattern recognitionArtificial Intelligence0202 electrical engineering electronic engineering information engineeringSegmentationCluster analysis021101 geological & geomatics engineeringRetrievalPixelbusiness.industryLinkageHyperspectral imagingPattern recognitionRemote sensingSupport vector machineMultiscale image segmentationHyperspectral imageryPixel ClassificationSignal Processing020201 artificial intelligence & image processingHyperspectral Data ClassificationComputer Vision and Pattern RecognitionArtificial intelligencebusinessAlgorithmsSoftwareModel
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Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Specie…

2018

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated …

Reflectance calibration010504 meteorology & atmospheric sciencesInfraredComputer sciencegeneettiset algoritmitUAVta1171Point clouddense point cloud01 natural scienceshyperspectral imagery; tree species recognition; photogrammetry; dense point cloud; reflectance calibration; UAV; random forest; genetic algorithm; machine learningilmakuvakartoitusMachine learninggenetic algorithmImage sensorfotogrammetria0105 earth and related environmental sciencesRemote sensingta113040101 forestryta213tree species recognitionspektrikuvausSpecies diversityHyperspectral imaging04 agricultural and veterinary sciencesOtaNanoreflectance calibrationDense point cloudVNIRRandom forestTree (data structure)hyperspectral imagerykoneoppiminenPhotogrammetryGenetic algorithmHyperspectral imageryPhotogrammetryTree species recognitionlajinmääritys0401 agriculture forestry and fisheriesGeneral Earth and Planetary SciencesRGB color modelkaukokartoituspuustorandom forestRandom forestRemote Sensing; Volume 10; Issue 5; Pages: 714
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Study of a plasma generated in metal surface treatment by laser ablation in air : characterisations of induced radiation and nanoparticules

2015

The interaction of a laser beam of short pulse duration with metallic materials such as aluminum, iron or titanium is characterized by the presence of a high energy and strongly ionized plume (plasma). The aim of this study is to understand the mechanisms involved in plasma, created when laser-target interaction, which lead to the formation of nanoparticles. We would generally consider the dynamic expansion in the air of the plasma plume formed with a nanosecond Nd:YAG laser to specify the conditions of formation of these particles and their morphological and structural characteristics. Thus, this work is divided onto two parts. In the first part, we present the experimental characterizatio…

S.AX.SGranulometry[PHYS.PHYS]Physics [physics]/Physics [physics]SpectrometrySurface treatmentNanoparticulesGranulométrieLaser[ PHYS.PHYS ] Physics [physics]/Physics [physics]Imagerie spectraleTraitement de surfacePlasmaNanoparticles[PHYS.PHYS] Physics [physics]/Physics [physics]SpectrométrieSpectral imagery
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A Constrained Band Selection Method Based on Information Measures for Spectral Image Color Visualization

2011

International audience; We present a new method for the visualization of spectral images, based on a selection of three relevant spectral channels to build a Red-Green-Blue composite. Band selection is achieved by means of information measures at the first, second and third orders. Irrelevant channels are preliminarily removed by means of a center-surround entropy comparison. A visualization-oriented spectrum segmentation based on the use of color matching functions allows for computational ease and adjustment of the natural rendering. Results from the proposed method are presented and objectively compared to four other dimensionality reduction techniques in terms of naturalness and informa…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing010504 meteorology & atmospheric sciencesBand SelectionComputer science0211 other engineering and technologies02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesRendering (computer graphics)Spectrum SegmentationData visualization[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingColor Matching FunctionsEntropy (information theory)Computer visionSegmentationElectrical and Electronic Engineering021101 geological & geomatics engineering0105 earth and related environmental sciencesVisualizationInformation Measuresbusiness.industryDimensionality reductionPattern recognitionImage segmentationVisualizationMulti/hyperspectral imageryGeneral Earth and Planetary SciencesArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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The classification of submerged vegetation using hyperspectral MIVIS data

2009

The aim of this research is to use hyperspectral MIVIS data to map the Posidonia oceanica prairies in a coastal lagoon (Stagnone di Marsala). It is approximately 12 km long and 2 km wide and is linked to the open sea by two shallow openings. This environment is characterised by prairies of phanerogams, the most common of which is Posidonia oceanica, an ideal habitat for numerous species of fish, molluscs and crustaceans. A knowledge of the distribution of submerged vegetation is useful to monitor the health of the lagoon. In order to classify the MIVIS imagery, the attenuation effects of the water column have been removed from the signal using Lyzenga’s technique. A comparison between class…

hyperspectral imagerSpectrometerbiologylcsh:QC801-809shallow watersubmerged vegetationHyperspectral imagingVegetationlcsh:QC851-999biology.organism_classificationwater column correctionWaves and shallow waterlcsh:Geophysics. Cosmic physicsGeophysicsWater columnhyperspectral imageryHabitatHomogeneousPosidonia oceanicaEnvironmental sciencelcsh:Meteorology. ClimatologyRemote sensingAnnals of Geophysics
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Mapping a-priori defined plant associations using remotely sensed vegetation characteristics

2014

Abstract Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has only recently been confronted by the remote sensing community. We present an approach to map the spatial distribution of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate between spectral reflectance and association occurrences. For a 12 km 2 study site in the Netherlands, the relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared (SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R 2 0.73, 0.64 and 0.76 for sali…

endmember selectionCalibration (statistics)Vegetation classificationcontinuous floristic gradientsSoil Scienceimaging spectroscopy/dk/atira/pure/sustainabledevelopmentgoals/clean_water_and_sanitationLaboratory of Geo-information Science and Remote SensingKrigingmoistureLaboratorium voor Geo-informatiekunde en Remote SensingComputers in Earth SciencesRemote sensingtropical forestsHyperspectral imagingGeologyVegetationPE&RCRegressionVNIRhyperspectral imageryclassificationaviris dataellenberg indicator valuesEnvironmental scienceregressionIndicator valueSDG 6 - Clean Water and SanitationRemote Sensing of Environment
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