Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression

Details

Serval ID
serval:BIB_7D45B638797D
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Title
Estimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression
Title of the conference
WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Author(s)
Stamenkovic J., Tuia D., De Morsier F., Borgeaud M., Thiran J.P.
Address
Gainesville, Florida, USA, June 25-28, 2013
Publication state
Published
Issued date
2013
Language
english
Abstract
In this paper, we propose to estimate soil moisture in
bare soils directly from hyperspectral imagery using
support vector regression (nu-SVR). nu-SVR is a
supervised non-parametric learning technique, e.g. making
no assumption on the underlying data distribution, which
shows good generalization properties even when only a
limited number of training samples is available (which is
often the case in soil moisture estimation). Estimation
in six tilled bare soil fields shows the potential of
using non-linear nu-SVR for the prediction of gravimetric
soil moisture. Dependence to the origin of training
samples, as well as their number, is thoroughly
considered.
Keywords
LTS5, Soil moisture, Hyperspectral, Support Vector, Regression, non linear, Bare soils
Create date
06/01/2014 21:46
Last modification date
20/08/2019 15:38
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