Search results for "hyperspectral"

showing 10 items of 271 documents

A method to estimate soil moisture from Airborne Hyperspectral Scanner (AHS) and ASTER data: Application to SEN2FLEX and SEN3EXP campaigns

2012

Abstract In this paper the soil moisture is estimated at airborne level and at satellite level by combining remotely sensed images with in situ measurements. At airborne level we process high-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor, and at satellite level we compute images acquired with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study has been accomplished in the framework of two field campaigns in the Barrax region (Spain): the SEN2FLEX (SENtinel-2 and FLuorescence EXperiment) campaign which was developed in July of 2005 and the SEN3EXP (Sentinel-3 Experiment) campaign which was carried out in June of 2009. The me…

Scanner010504 meteorology & atmospheric sciencesMean squared errorMeteorology0211 other engineering and technologiesSoil ScienceHyperspectral imagingGeology02 engineering and technology01 natural sciencesNormalized Difference Vegetation IndexAdvanced Spaceborne Thermal Emission and Reflection RadiometerEmissivityEnvironmental scienceSatelliteComputers in Earth SciencesWater content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

2009

Abstract. The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for …

ScannerMeteorology010504 meteorology & atmospheric sciencestélédétection[SDV]Life Sciences [q-bio]0211 other engineering and technologiesevapotranspirationREMOTE SENSING;SURFACE TEMPERATURE;INFRAREDévapotranspirationespagne02 engineering and technology01 natural scienceslcsh:Technologylcsh:TD1-1066REMOTE SENSINGEvapotranspirationtempératureEmissivityWageningen Environmental ResearchCGI - Earth Observationlcsh:Environmental technology. Sanitary engineeringlcsh:Environmental sciencesRemote sensing021101 geological & geomatics engineering0105 earth and related environmental scienceslcsh:GE1-350algorithmCGI - Aardobservatielcsh:TNear-infrared spectroscopylcsh:Geography. Anthropology. RecreationHyperspectral imagingINFRAREDCL - Urban and Regional DevelopmentSpectral bandspays méditerranéenVNIRbilan radiatiflcsh:GRemote sensing (archaeology)[SDE]Environmental SciencesEnvironmental scienceSURFACE TEMPERATUREeuropeland-surface temperatureCL - Stadsregionale Ontwikkeling
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Global sensitivity analysis of the A-SCOPE model in support of future FLEX fluorescence retrievals

2014

In support of ESA's Earth Explorer 8 candidate mission FLEX (FLuorescence EXplorer), a Photosynthesis Study has been initiated to quantitatively link fluorescence to photosynthesis. This led to the development of A-SCOPE, a graphical user interface software package that integrates multiple biochemical models into the soil-vegetation-atmosphere-transfer model SCOPE. Its latest version (v1.53) has been successfully verified and was subsequently evaluated through a global sensitivity analysis. By using the method of Saltelli [4], the relative importance of each input variable to model outputs was quantified through first order and total effect sensitivity indices. Variations in leaf area index…

Scope (project management)Computer sciencebusiness.industryHyperspectral imagingSet (abstract data type)FLEXVariable (computer science)global sensitivity analysiSignal ProcessingFLEXSensitivity (control systems)fluorescenceLeaf area indexbusinessA-SCOPEGraphical user interfaceRemote sensing1707
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Using active learning to adapt remote sensing image classifiers

2011

The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and cluster…

Selection biasActive learningCovariate shiftPixelContextual image classificationComputer scienceImage classificationmedia_common.quotation_subjectSoil ScienceHyperspectral imagingGeologyMaximizationLand coverRemote sensingHyperspectralVHRComputers in Earth SciencesCluster analysisClassifier (UML)Remote sensingmedia_common
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Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX fiel…

2008

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land-surface temperature (LST) and emissivity (LSE) from remotely-sensed data. The proposed method is based on a synergistic usage of the split-window (SW) algorithm and the two-temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel-2 and Fluorescence Experiment (SEN2FLEX) field c…

Set (abstract data type)ScannerMean squared errorThermalSeparation (aeronautics)EmissivityGeneral Earth and Planetary SciencesEnvironmental scienceHyperspectral imagingField campaignRemote sensingInternational Journal of Remote Sensing
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A Comparative Study and an Evaluation Framework of Multi/Hyperspectral Image Compression

2009

In this paper, we investigate different approaches for multi/hyperspectral image compression. In particular, we compare the classic multi-2D compression approach and two different implementations of 3D approach (full 3D and hybrid) with regards to variations in spatial and spectral dimensions. All approaches are combined with a weighted Principal Component Analysis (PCA) decorrelation stage to optimize performance. For consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR, including eight metrics divided into three families. The results show the weaknesses and strengths of each approach.

Set partitioning in hierarchical treesWaveletPixelbusiness.industryPrincipal component analysisMultispectral imageWavelet transformHyperspectral imagingPattern recognitionArtificial intelligencebusinessDecorrelationMathematics2009 Fifth International Conference on Signal Image Technology and Internet Based Systems
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Multi-temporal and Multi-source Remote Sensing Image Classification by Nonlinear Relative Normalization

2016

Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corres…

Signal Processing (eess.SP)FOS: Computer and information sciences010504 meteorology & atmospheric sciencesHyperspectral imagingComputer Vision and Pattern Recognition (cs.CV)0211 other engineering and technologiesNormalization (image processing)Computer Science - Computer Vision and Pattern Recognition02 engineering and technology3107 Atomic and Molecular Physics and Optics01 natural sciencesLaboratory of Geo-information Science and Remote SensingComputer vision910 Geography & travelMathematicsDomain adaptationContextual image classificationImage and Video Processing (eess.IV)1903 Computers in Earth SciencesPE&RCClassificationAtomic and Molecular Physics and OpticsComputer Science ApplicationsKernel method10122 Institute of GeographyKernel (image processing)Feature extractionFeature extractionVery high resolutionGraph-based methods1706 Computer Science ApplicationsFOS: Electrical engineering electronic engineering information engineeringLaboratorium voor Geo-informatiekunde en Remote SensingComputers in Earth SciencesElectrical Engineering and Systems Science - Signal ProcessingEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingManifold alignmentbusiness.industryNonlinear dimensionality reductionHistogram matchingKernel methodsPattern recognitionElectrical Engineering and Systems Science - Image and Video ProcessingManifold learningArtificial intelligence2201 Engineering (miscellaneous)businessISPRS Journal of Photogrammetry and Remote Sensing
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A fluorescence retrieval method for the flex sentinel-3 tandem mission

2014

A new fluorescence retrieval method is proposed to support ESA's 8th Earth Explorer Fluorescence EXplorer (FLEX) candidate mission. Most hyperspectral fluorescence retrieval algorithms available in the literature are very sensitive to true reflectance modelization and/or they assume the atmospheric status as known. The proposed algorithm delivers the retrieval of full fluorescence spectrum at canopy level by using only Top Of Atmosphere (TOA) radiances as input. The proposed method starts with (1) the atmospheric correction of TOA radiances, characterizing the state of the atmosphere without assuming any a-priori classification on aerosols models, (2) performing a first estimation of fluore…

Signal processingComputer sciencesynergy productFluorescence retrievalAtmospheric correctionHyperspectral imagingAtmospheric modelFluorescenceFLEXAtmosphereAtmospheric correctionSentinel-3Adaptive opticsAbsorption (electromagnetic radiation)Remote sensing2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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Salient Pixels and Dimensionality Reduction for Display of Multi/Hyperspectral Images

2012

International audience; Dimensionality Reduction (DR) of spectral images is a common approach to different purposes such as visualization, noise removal or compression. Most methods such as PCA or band selection use either the entire population of pixels or a uniformly sampled subset in order to compute a projection matrix. By doing so, spatial information is not accurately handled and all the objects contained in the scene are given the same emphasis. Nonetheless, it is possible to focus the DR on the separation of specific Objects of Interest (OoI), simply by neglecting all the others. In PCA for instance, instead of using the variance of the scene in each spectral channel, we show that i…

Spectral Images[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingChannel (digital image)Computer scienceMultispectral image0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingProjection (linear algebra)[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineeringIAPRComputer vision021101 geological & geomatics engineeringSaliencyPixelbusiness.industryDimensionality reductionHyperspectral imagingPattern recognitionDimensionality reductionVisualizationComputer Science::Computer Vision and Pattern Recognition020201 artificial intelligence & image processingArtificial intelligenceFocus (optics)business[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems

2013

Abstract Leaf area index (LAI) is a key biophysical parameter for the monitoring of agroecosystems. Conventional two-band vegetation indices based on red and near-infrared relationships such as the normalized difference vegetation index (NDVI) are well known to suffer from saturation at moderate-to-high LAI values (3–5). To bypass this saturation effect, in this work a robust alternative has been proposed for the estimation of green LAI over a wide variety of crop types. By using data from European Space Agency (ESA) campaigns SPARC 2003 and 2004 (Barrax, Spain) experimental LAI values over 9 different crop types have been collected while at the same time spaceborne imagery have been acquir…

Spectral indexSoil ScienceRed edgeHyperspectral imagingSatellitePlant SciencePrecision agricultureVegetationLeaf area indexAgronomy and Crop ScienceNormalized Difference Vegetation IndexMathematicsRemote sensingEuropean Journal of Agronomy
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