AN ARTIFICIAL NEURAL-NETWORK FOR INVERSION OF VEGETATION PARAMETERS FROM RADAR BACKSCATTER COEFFICIENTS

被引：7

作者：

CHUAH, HT

机构：

[1] Department of Electrical Engineering University of Malaya

来源：

JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS | 1993年 / 7卷 / 08期

关键词：

D O I：

10.1163/156939393X00976

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：

An inverse scattering model based on an artificial neural network using the back-error propagation technique is presented in this paper. The vegetation canopy is modelled as a half-space of randomly distributed and orientated circular disks, representing the leaves. A Monte Carlo model is used as the forward scattering model to calculate the radar backscatter coefficients. Using different sets of input parameters such as leaf moisture content, frequency and size of leaf, this forward model generates about 2000 sets of backscatter coefficients. These sets of backscatter coefficients and the corresponding input parameters form a training set for the neural network. The trained neural network constitutes an inverse scatter model. Given a set of backscatter coefficients, our model is able to estimate simultaneously the leaf moisture content, the radius and the thickness of the circular leaf within an accuracy of 10%.

引用

页码：1075 / 1092

页数：18

共 29 条

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