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Improving hyperspectral classification based on wavelet decomposition
Year:
2007
Authors :
Alchanatis, Victor
;
.
Volume :
Co-Authors:
Almog, O., Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel
Shoshany, M., Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO - The Volcani Center, Bet Dagan, Israel
Facilitators :
From page:
3806
To page:
3809
(
Total pages:
4
)
Abstract:
Information extraction from Hyperspectral imagery is highly affected by difficulties in accounting for flux density variation and Bidirectional reflectance effects. Calculation of flux density requires digital description of the surface structure at the pixel level, which is frequently not available at the accuracy required (if exists). The result of these shortcomings in achieving accurate radiometric image calibration is reduced separability of surface types: limiting the performance of spectral classification schemes. In this study an alternative approach is presented: application of features of the spectral signature which mainly represent the shape of the spectral curve. This is achieved by applying features calculated based on Wavelet decomposition. © 2007 IEEE.
Note:
Related Files :
calibration
Flux density
Hyperspectral
illumination
image classification
Lighting
remote sensing
Surface structure
Wavelet coefficients
Show More
Related Content
More details
DOI :
10.1109/IGARSS.2007.4423672
Article number:
4423672
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
29327
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:45
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Scientific Publication
Improving hyperspectral classification based on wavelet decomposition
Almog, O., Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel
Shoshany, M., Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO - The Volcani Center, Bet Dagan, Israel
Improving hyperspectral classification based on wavelet decomposition
Information extraction from Hyperspectral imagery is highly affected by difficulties in accounting for flux density variation and Bidirectional reflectance effects. Calculation of flux density requires digital description of the surface structure at the pixel level, which is frequently not available at the accuracy required (if exists). The result of these shortcomings in achieving accurate radiometric image calibration is reduced separability of surface types: limiting the performance of spectral classification schemes. In this study an alternative approach is presented: application of features of the spectral signature which mainly represent the shape of the spectral curve. This is achieved by applying features calculated based on Wavelet decomposition. © 2007 IEEE.
Scientific Publication
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