Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (12): 2859-2862.

• 传感器与信号处理 • 上一篇    下一篇

基于多特征空间与神经网络的SAR图像识别方法

杨露菁,郝威,刘忠,王德石   

  1. 海军工程大学电子工程学院, 湖北 武汉 430033
  • 出版日期:2009-12-24 发布日期:2010-01-03

SAR image recognition method based on multieigenspace and neural network

YANG Lu-jing,HAO Wei,LIU Zhong,WANG De-shi   

  1. Electronic Engineering Coll., Naval Univ. of Engineering, Wuhan 430033, China
  • Online:2009-12-24 Published:2010-01-03

摘要:

同一目标在不同方位角下的SAR图像有很大变化,这使得基于单视角图像进行SAR图像目标识别具有很大难度。针对这一问题,提出了一种基于多个独立分量分析特征空间及神经网络集成的SAR图像目标识别系统。通过独立分量分析构造若干个方位角的SAR图像特征空间,为每一特征空间各训练一个模糊极小极大神经网络用于分类,然后利用D-S证据理论集成各个神经网络的输出。仿真结果表明,与利用精确的方位角估计预处理选择最佳单一神经网络相比,该方法可以获得更高的识别精度,且不需要进行方位角估计预处理。

Abstract:

SAR images vary with different aspects greatly even for the same target, which makes it difficult to identify targets from SAR images based on a single aspect. To solve the problem, an SAR image recognition system based on multipleeigenspaces of ICA and fuzzy minmax neural network ensemble is proposed. Some SAR image eigenspaces are constructed for different aspects based on independent component analysis. An independent neural network is trained for each of eigenspace of different aspects. The trained neural networks are used to recognize SAR targets, and their results are combined through D-S evidence reasoning. The simulation results show that the recognition accuracy is higher than that of using a single neural network with aspect estimation preprocessing. Moreover, it does not need aspect estimation preprocessing.