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基于纹理和BP神经网络的SAR图像分类
引用本文:李海权,李春霞,吴彩银,胡召玲,钱小龙.基于纹理和BP神经网络的SAR图像分类[J].遥感信息,2009(3):58-63.
作者姓名:李海权  李春霞  吴彩银  胡召玲  钱小龙
作者单位:1. 徐州师范大学城市与环境学院,徐州,221116;广西玉林市玉林高中,广西玉林,537000
2. 广西师范大学教育科学院,桂林,541004
3. 徐州师范大学城市与环境学院,徐州,221116
基金项目:江苏省高校自然科学研究计划 
摘    要:研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明:纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。

关 键 词:纹理  灰度共生矩阵  BP神经网络  SAR图像  最大似然法

Study on SAR Image Classification Based on Texture and BP Neural Network
LI Hai-quan,LI Chun-xia,WU Cai-yin,HU Zhao-ling,QIAN Xiao-long.Study on SAR Image Classification Based on Texture and BP Neural Network[J].Remote Sensing Information,2009(3):58-63.
Authors:LI Hai-quan  LI Chun-xia  WU Cai-yin  HU Zhao-ling  QIAN Xiao-long
Affiliation:LI Hai-quan , LI Chun-xia , WU Cai-yin , HU Zhao-ling , QIAN Xiao-long (1.College of Urban and Environment Science, XuZhou Normal University, XuZhou 221116; (2.College of Educational Science ,GuangXi Normal University, GuiLin 541004; (3.GuangXi YuLin High School, Yulin Guangxi 53700)
Abstract:SAR image classification based on texture and BP neural network is researched in this paper. Firstly, the speckles from SAR image are eliminated by the enhanced FROST filter algorithm. Then the texture features of the denoised SAR image are extracted based on gray co-occurrence matrix, and effective texture features are screened through a large number of experi- ments. At last, combining with texture features, SAR image is classified with the classical maximum likelihood and BP neural network. The results indicate the following: considering texture information supporting, the classification accuracy of SAR im- age is greatly enhanced. The classification accuracy of SAR image based on BP neural network is higher than maximum likeli- hood method.
Keywords:texture  gray-level co-occurrence matrix  BP neural network  SAR image  maximum likelihood
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