000185220 001__ 185220
000185220 005__ 20180913061820.0
000185220 037__ $$aCONF
000185220 245__ $$aEstimation of Soil Moisture from Airborne Hyperspectral Imagery with Support Vector Regression
000185220 269__ $$a2013
000185220 260__ $$c2013
000185220 336__ $$aConference Papers
000185220 520__ $$aIn 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.
000185220 6531_ $$aLTS5
000185220 6531_ $$aSoil moisture
000185220 6531_ $$aHyperspectral
000185220 6531_ $$aSupport Vector Regression
000185220 6531_ $$anon linear
000185220 6531_ $$aBare soils
000185220 700__ $$0244611$$aStamenkovic, Jelena$$g195175
000185220 700__ $$0245927$$aTuia, Devis$$g150680
000185220 700__ $$0242940$$aDe Morsier, Frank$$g166738
000185220 700__ $$aBorgeaud, Maurice
000185220 700__ $$0240323$$aThiran, Jean-Philippe$$g115534
000185220 7112_ $$aWorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)$$cGainesville, Florida, USA$$dJune 25-28, 2013
000185220 909C0 $$0252394$$pLTS5$$xU10954
000185220 909C0 $$0252045$$pLASIG$$xU10244
000185220 909CO $$ooai:infoscience.tind.io:185220$$pconf$$pSTI$$pENAC
000185220 917Z8 $$x166738
000185220 917Z8 $$x115534
000185220 937__ $$aEPFL-CONF-185220
000185220 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000185220 980__ $$aCONF