Remote sensing image classification based on evidence theory and neural networks

被引：0
|
作者：
Chen, G [1 ]
Li, BC [1 ]
Guo, ZG [1 ]
机构：
[1] Informat Engn Univ, Zhengzhou 450002, Henan, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 | 2004年 / 3173卷
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：
An effective method based on evidence theory and neural networks to classify the remote sensing images is brought forward in the paper. Firstly, with the spatial information in consideration, the original image is smoothed with a modified gradient inverse weighting smoothing method, then the classification of the original and smoothed images is performed separately using a BP neural network. Finally, result comes out after fusing the two classification results with D-S evidence theory. Experimental results demonstrate that the proposed method is effective and can improve the classification accuracy.
引用
收藏
页码：971 / 976
页数：6
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