Change detection of SAR images using morphologic attribute profile and support vector machine

ZHANG Xiong-mei; YI Zhao-xiang; TIAN Song; SONG Jian-she

doi:10.3788/OPE.20142210.2832

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Change detection of SAR images using morphologic attribute profile and support vector machine

更新时间：2020-08-13

- Change detection of SAR images using morphologic attribute profile and support vector machine
- Optics and Precision Engineering Vol. 22, Issue 10, Pages: 2832-2839(2014)
- 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：
- DOI：10.3788/OPE.20142210.2832
  CLC： TP751
- Received：13 March 2014，
  
  Revised：20 April 2014，
  
  Published：25 October 2014
- 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

PAUL L, RAMAMOORTHY D P. Synthetic aperture radar image change detection using fuzzy C-means clustering algorithm[J]. International Journal of Advanced Research in Computer and Communication Engineering, 2013, 2(3): 1374-1379.

MEGHANA P M K, RAVIKUMAR A V. Robust technique for change detection in multitemporal synthetic aperture radar images[J]. International Journal of Scientific & Engineering Research, 2013, 4(7): 2228-2235.

MAS J F. Monitoring land-cover changes: a comparison of change detection techniques [J]. International Journal of Remote Sensing, 1999, 20(1): 139-152.

FRANSSON J E S, WALTER F, BLENNOW K, et al.. Detection of storm-damaged forested areas using airborne CARABAS-II VHF SAR image data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10): 2170-2175.

RIDD M K,LIU J J. A comparison of four algorithms for change detection in an urban environment [J]. Remote Sensing Environment, 1998, 63(2):95-100.

李金基, 焦李成, 张向荣,等. 基于融合和T-分布的SAR图像水灾变化检测[J]. 计算机研究与发展, 2011, 48(2): 271-280. LI J J, JIAO L CH, ZHANG X R, et al.. Detection for flood change with SAR images based on fusion and T-distribution[J]. Journal of Computer Research and Development, 2011, 48(2): 271-280. (in Chinese)

胡召玲. 广义高斯模型及KI双阈值法的SAR图像非监督变化检测[J]. 测绘学报, 2013, 42(1): 116-122. HU ZH L. An unsupervised change detection approach based on KI dual thresholds under the generalized Gauss model assumption in SAR images[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 116-122. (in Chinese)

李阳阳, 吴娜娜, 焦李成,等. 基于量子免疫克隆聚类的SAR图像变化检测[J]. 红外与毫米波学报, 2011, 30(4): 372-376. LI Y Y, WU N N, JIAO L CH, et al.. Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm[J]. Journal of Infrared and Millimeter Waves, 2011, 30(4):372-376.(in Chinese)

何志勇, 孙立宁, 黄伟国,等. 基于Otsu准则和直线截距直方图的阈值分割[J]. 光学精密工程, 2012, 20(10): 2315-2323. HE ZH Y, SUN L N, HUANG W G, et al.. Thresholding segmentation algortithm based on Otsu criterion and line intercept histogram[J]. Opt. Precision Eng., 2012, 20(10): 2315-2323. (in Chinese)

KITTLER J, ILLINGWORTH J. Minimum error thresholding[J]. Pattern Recognition, 1986, 19(1): 41-47.

BAZI Y, BRUZZONE L, MELGANI F. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 874-887.

DALLAMURA M, BENEDIKTSSON J A, RNWASKE B, et al.. Extended profiles with morphological attribute filters for the analysis of hyperspectral data [J]. International Journal of Remote Sensing, 2010, 31(22): 5975-5991.

张宇洋,刘满华,韩韬. 基于Mean Shift图像分割和支持向量机判决的候梯人数视觉检测系统[J]. 光学精密工程, 2013, 21(4): 1079-1085. ZHANG Y Y, LIU M H, HAN T. Elevator-waiting people counting system based on Mean Shift segmentation and SVM classification[J]. Opt. Precision Eng., 2013, 21(4): 1079-1085. (in Chinese)

李伟红, 龚卫国, 杨利平,等. 人脸特征选择中的SVM泛化误差估计[J]. 光学精密工程, 2008, 16(8):1452-1458. LI W H, GONG W G, YANG L P, et al.. SVM generalization error estimation for facial feature selection [J]. Opt. Precision Eng., 2008, 16(8): 1452-1458. (in Chinese)

CELIK T. Multiscale change detection in multitemporal satellite images[J]. IEEE Geoscience Remote Sensing Letters, 2009, 6(4): 820-824.

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