0000000000459296

AUTHOR

Francois Tavin

showing 5 related works from this author

Comparison of metrics to remove the influence of geometrical conditions on soil reflectance

2007

The objective of this work is to find the best metric to ignore the variations of soil reflectance induced by the solar-view angles geometry. Differences between spectra measured for the same soil under different observation and illumination configurations can leads to misclassifications. Using ninety two soils of different composition measured under twenty eight solar- view angles geometries, we tested 3 metrics : RMSE, SAM, R2 (the coefficient of determination) and we compared their performances. The best metric seems to be the coefficient of determination with 93 % of good classifications.

Coefficient of determinationMean squared errorSoil waterMultispectral imageMetric (mathematics)Surface roughnessHyperspectral imagingReflectivityRemote sensingMathematics2007 IEEE International Geoscience and Remote Sensing Symposium
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Optimization of image parameters using a hyperspectral library application to soil identification and moisture estimation

2009

The growing number of sensors raises questions about the image parameters required for the application, soil identification and moisture estimation. Hyperspectral images are also known to contain highly redundant information. Hence not all the spectral bands are needed for the satisfactory classification of the soil types. Hence, the work was aimed at obtaining these optimal spectral bands for identifying the soil types and to use these spectral bands to estimate the moisture content of the soils using the method proposed by Whiting et.al.

Identification (information)MoistureSoil waterEnvironmental scienceHyperspectral imagingFeature selectionSoil classificationSpectral bandsWater contentPhysics::GeophysicsRemote sensing2009 IEEE International Geoscience and Remote Sensing Symposium
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Comparison of Metrics for the Classification of Soils Under Variable Geometrical Conditions Using Hyperspectral Data

2008

International audience; The objective of this letter is to find a distance metric between reflectance spectra that is not sensitive to the variations on the soil reflectance induced by the geometry of solar-view angles. This is motivated by the fact that differences between spectra measured for the same soil under different observation and illumination configurations can lead to misclassifications. Using 26 soils of different compositions simulated with Hapke’s model and 92 soils of different compositions measured under 28 solarview angle geometries in laboratory conditions, we tested three metrics, namely, root-mean-square error, spectral angle mapper, and R2 (the coefficient of determinat…

Coefficient of determination010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesSOIL IDENTIFICATION02 engineering and technologySolid modeling01 natural sciencesSpectral lineCLASSIFICATION[SPI]Engineering Sciences [physics]HYPERSPECTRALSurface roughnessElectrical and Electronic EngineeringComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingHyperspectral imagingSoil classificationGeotechnical Engineering and Engineering GeologySOLAR-VIEW ANGLESoil waterSPECTRAL LIBRARYDISTANCE METRIC[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Optimal band selection for future satellite sensor dedicated to soil science

2009

Hyperspectral imaging systems could be used for identifying the different soil types from the satellites. However, detecting the reflectance of the soils in all the wavelengths involves the use of a large number of sensors with high accuracy and also creates a problem in transmitting the data to earth stations for processing. The current sensors can reach a bandwidth of 20 nm and hence, the reflectance obtained using the sensors are the integration of reflectance obtained in each of the wavelength present in the spectral band. Moreover, not all spectral bands contribute equally to classification and hence, identifying the bands necessary to have a good classification is necessary to reduce …

Statistical classificationContextual image classificationComputer scienceBandwidth (signal processing)Hyperspectral imagingSatelliteFeature selectionSpectral bandsData transmissionRemote sensing2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
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Comparison of metrics for the classification of soils under variable geometrical conditions using hyperspectral data

2008

International audience; no abstract

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputingMilieux_MISCELLANEOUS
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