0000000000445226

AUTHOR

Audrey Roman

showing 3 related works from this author

Robustness of texture parameters for color texture analysis

2006

This article proposes to deal with noisy and variable size color textures. It also proposes to deal with quantization methods and to see how such methods change final results. The method we use to analyze the robustness of the textures consists of an auto-classification of modified textures. Texture parameters are computed for a set of original texture samples and stored into a database. Such a database is created for each quantization method. Textures from the set of original samples are then modified, eventually quantized and classified according to classes determined from a precomputed database. A classification is considered incorrect if the original texture is not retrieved. This metho…

business.industryCovariance matrixAutocorrelationComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPattern recognitionMaxima and minimaQuantization (physics)Matrix (mathematics)Computer Science::GraphicsAutocorrelation matrixComputer Science::Computer Vision and Pattern RecognitionPrincipal component analysisRGB color modelComputer visionArtificial intelligencebusinessComputingMethodologies_COMPUTERGRAPHICSMathematicsSPIE Proceedings
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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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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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