Novel features for polarimetric SAR image classification by neural network

被引：0

作者：

Khan, KU ^{[1
]}

Yang, J ^{[1
]}

机构：

[1] Tsing Hua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年

关键词：

undecimated discrete wavelet transform (UDWT); neural network; principal component analysis (PCA);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a set of effective features derived from the coherence matrix of polarimetric SAR data. Neural network is used as the classification engine. The maximum likelihood estimator (MLE) result is used as the reference to compare the result of the proposed method. It is demonstrated that the average classification accuracy by the proposed method is more than that by the MLE. The maximum overall efficiency obtained by the proposed method is 95.4%.

引用

页码：165 / 170

页数：6

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