Multi-spectral image classification using adaptive neural network

被引：0
|
作者：
Li, R [1 ]
Lebby, G [1 ]
Sherrod, E [1 ]
Baghavan, S [1 ]
机构：
[1] N Carolina Agr & Tech State Univ, Dept Elect Engn, NASA ACE Ctr, Greensboro, NC 27411 USA
来源：
PROCEEDINGS OF THE FIFTH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1 AND 2 | 2000年
关键词：
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：
Multispectral classification is very important in remote sensing areas. Multi-layer backpropagation is used regularly to classify the data. However, it suffers from slow convergence rate for training. In this paper, we are presenting adaptive methods to train the backpropagation network both. locally and globally. The result is a fast convergence rate without any sacrifice on the part of classification accuracy. In addition, we are evaluating the adaptive version of the radial basis function (RBF) method for its versatility of classifying multispectral data.
引用
收藏
页码：A391 / A394
页数：4
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