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Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation
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AI
DOI：10.1109/LGRS.2011.2109934.png)
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Abstract
En 中文
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 e-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.
Keywords:
Biophysical parameter estimation
model inversion
regression
support vector regression (SVR)
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16.4
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