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Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation

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5 Author(s)
Tuia, D. ; Image Process. Lab., Univ. de Valencia, València, Spain ; Verrelst, J. ; Alonso, L. ; Perez-Cruz, F.
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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.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:8 ,  Issue: 4 )

Date of Publication:

July 2011

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