结合形态学属性断面与支持向量机的合成孔径雷达图像变化检测

张雄美; 易昭湘; 田淞; 宋建社

doi:10.3788/OPE.20142210.2832

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结合形态学属性断面与支持向量机的合成孔径雷达图像变化检测

信息科学 | 更新时间：2020-08-13

- 结合形态学属性断面与支持向量机的合成孔径雷达图像变化检测
- Change detection of SAR images using morphologic attribute profile and support vector machine
- 光学精密工程 2014年22卷第10期页码：2832-2839
- 作者机构：
  
  1. 第二炮兵工程大学,陕西西安,710025
  2. 重庆通信学院重庆,400035
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.61072141,No.61132008)()
- DOI：10.3788/OPE.20142210.2832
  中图分类号： TP751
- 收稿日期：2014-03-13，
  
  修回日期：2014-04-20，
  
  纸质出版日期：2014-10-25
- 稿件说明：
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张雄美, 易昭湘, 田淞等. 结合形态学属性断面与支持向量机的合成孔径雷达图像变化检测[J]. 光学精密工程, 2014,22(10): 2832-2839

ZHANG Xiong-mei, YI Zhao-xiang, TIAN Song etc. Change detection of SAR images using morphologic attribute profile and support vector machine[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2832-2839
张雄美, 易昭湘, 田淞等. 结合形态学属性断面与支持向量机的合成孔径雷达图像变化检测[J]. 光学精密工程, 2014,22(10): 2832-2839 DOI： 10.3788/OPE.20142210.2832.

ZHANG Xiong-mei, YI Zhao-xiang, TIAN Song etc. Change detection of SAR images using morphologic attribute profile and support vector machine[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2832-2839 DOI： 10.3788/OPE.20142210.2832.

摘要

针对传统合成孔径雷达(SAR)图像变化检测方法存在误差大、检测率低等问题

提出了一种基于形态学属性断面(MAP)的SAR图像变化检测方法.该方法利用MAP算法提取差异图像的几何结构特征

构造深入描述图像结构化信息的特征向量空间;在利用阈值法对图像进行分割的基础上

引入偏移因子

实现训练样本的自动选取;最后

用支持向量机(SVM)在多维特征空间中对图像进行变化类与非变化类的分类.实验结果显示:本文算法的检测结果优于基于高斯模型的KI阈值法(GM_KI)、基于广义高斯模型的KI阈值法(GGM_KI)和大津法(Otsu)等3种阈值法的检测结果

Kappa系数保持在0.87以上;当峰值信噪比(PSNR)介于[29

44]dB时

抗噪性能指标保持在0.97以上.这些结果证明了文中方法的有效性和优越性.

Abstract

As classical change detection methods for Synthetic Aperture Radar (SAR) images have high error rates and low detection rates

a novel change detection method of SAR images based on Morphology Attribute Profile (MAP) was proposed. The MAP algorithm was employed to extract the geometric features of the difference images and a feature vector space was constructed to describe the image inherent structure. Then

the offsets were introduced to select the training samples automatically based on the segmentation of different images by using thresholding method. Finally

Support Vector Machine (SVM) was used to distinguish changed pixels from unchanged pixels in the multidimensional feature space. Experiment results show that the proposed method achieves better performance than the KI threshold selection criterion based on Gaussian model (GM_KI)

KI threshold selection criterion based on general Gaussian model(GGM_KI) and Otsu methods

the lowest Kappa is 0.87

and the lowest anti-noise is 0.97 when the Peak Signal to Noise Ratio(PSNR) belongs to [29

44] dB. These results verify the effectiveness and superiority of the proposed method.

关键词

Keywords

references

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