Polarimetric SAR data classification using competitive neural networks

被引：23
|
作者：
Ito, Y [1 ]
机构：
[1] Takamatsu Natl Coll Technol, Dept Civil Engn, Kagawa 7618058, Japan
[2] Univ Osaka Prefecture, Coll Engn, Dept Comp Sci & Syst, Osaka 5998531, Japan
来源：
INTERNATIONAL JOURNAL OF REMOTE SENSING | 1998年 / 19卷 / 14期
关键词：
D O I：
10.1080/014311698214442
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
A classification method for polarimetric SAR data analysis using a competitive neural network is considered. The network is trained by two LVQ algorithms. In addition, a specific feature vector as the input for the network employing the JM distance is determined. As a result of experiments using SIR-C data, average accuracy for classification results was 86.40%, where (i) the competitive neural network with 8-input and 40-output neurons was trained by LVQ1 and LVQ2.1, and (ii) the 8-dimensional feature vector with backscattering coefficients (dB) and pseudo-relative phases between HH and VV from L and C bands was used. It is shown that the proposed method outperforms other methods in average accuracy.
引用
收藏
页码：2665 / 2684
页数：20
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