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Shoshany, M., Division of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
Almog, O., Division of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet-Dagan 50250, Israel
The inaccurate estimation of the incoming solar flux density, owing to the lack of adequate representation of the surface orientation, causes the high variability of the spectral reflectance of a given surface material and, consequently, confusion between different materials. A new generic solution is presented in this letter, based on spectral parameterization techniques derived from wavelet analysis. Significant classification improvements were obtained for both synthetic data and the data acquired by hyperspectral imaging of Mediterranean vegetation. © 2006 IEEE.
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Wavelet decomposition for reducing flux density effects on hyperspectral classification
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Shoshany, M., Division of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
Almog, O., Division of Transportation and Geo-Information Engineering, Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet-Dagan 50250, Israel
Wavelet decomposition for reducing flux density effects on hyperspectral classification
The inaccurate estimation of the incoming solar flux density, owing to the lack of adequate representation of the surface orientation, causes the high variability of the spectral reflectance of a given surface material and, consequently, confusion between different materials. A new generic solution is presented in this letter, based on spectral parameterization techniques derived from wavelet analysis. Significant classification improvements were obtained for both synthetic data and the data acquired by hyperspectral imaging of Mediterranean vegetation. © 2006 IEEE.
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