CROP BACKSCATTER MODELING AND SOIL MOISTURE ESTIMATION WITH SUPPORT VECTOR REGRESSION

被引:1
|
作者
Stamenkovic, Jelena [1 ]
Ferrazzoli, Paolo
Guerriero, Leila
Tuia, Devis
Thiran, Jean-Philippe [1 ]
Borgeaud, Maurice
机构
[1] Ecole Polytech Fed Lausanne, Signal Proc Lab LTS5, CH-1015 Lausanne, Switzerland
来源
2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2014年
关键词
Crop backscatter; soil moisture; SVR;
D O I
10.1109/IGARSS.2014.6947166
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, nonparametric learning technique, u-SVR. u-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.
引用
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页数:4
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