Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation

This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output S...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2011-07, Vol.8 (4), p.804-808
Main Authors: Tuia, D., Verrelst, J., Alonso, L., Perez-Cruz, F., Camps-Valls, G.
Format: Article
Language:English
Subjects:
Biological system modeling
Biomedical imaging
Biophysical parameter estimation
Estimation
Inversions
Mathematical analysis
Mathematical models
model inversion
Parameter estimation
Regression
Remote sensing
Support vector machines
support vector regression (SVR)
Vectors (mathematics)
Vegetation mapping
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Summary:This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an ε-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2011.2109934