计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (9): 140-146.doi: 10.11896/jsjkx.230800014

• 计算机图形学&多媒体 • 上一篇    下一篇

基于演化多目标聚类的SAR图像变化检测

周宇1, 杨俊岭2, 党可林1   

  1. 1 西安电子科技大学电子工程学院 西安 710071
    2 军事科学院军事科学信息研究中心 北京 100142
  • 收稿日期:2023-08-03 修回日期:2024-07-01 出版日期:2024-09-15 发布日期:2024-09-10
  • 通讯作者: 杨俊岭(20y02@sohu.com)
  • 作者简介:(zhouyu@xidian.edu.cn)

Change Detection in SAR Images Based on Evolutionary Multi-objective Clustering

ZHOU Yu1, YANG Junling2, DANG Kelin1   

  1. 1 School of Electronic Engineering,Xidian University,Xi'an 710071,China
    2 Military Science Information Research Center,Academy of Military Sciences,Beijing 100142,China
  • Received:2023-08-03 Revised:2024-07-01 Online:2024-09-15 Published:2024-09-10
  • About author:ZHOU Yu,born in 1983,Ph.D.His main research interests include machine learning and evolutionary computation.
    YANG Junling,born in 1975,Ph.D.His main research interest is national defense artificial intelligence.

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摘要： 基于合成孔径雷达(SAR)图像的变化检测是遥感领域中一项具有挑战性的任务,如何在噪声鲁棒性和有效保留细节之间取得平衡是一个迫切需要解决的问题。然而,大多数SAR图像变化检测方法为了更好地抑制斑点噪声,不可避免地会在一定程度上丢失图像细节。为了解决这一问题,提出了一种基于演化多目标聚类的SAR图像变化检测多目标聚类算法,将变化检测问题转化为一个多目标优化问题。该方法同时构建了两个相互冲突的目标,即分别基于原始差异图与噪声滤波后差异图的聚类能量函数,并用基于分解的演化多目标优化算法MOEA/D对以上目标函数进行优化,实现对差异图不变区域与变化区域的聚类。利用该技术可得到一组变化检测图,用户可以根据自己的需求选择合适的结果。最后,在两个SAR图像数据集上进行了充分的实验,结果表明了该方法的有效性。

关键词: SAR图像, 变化检测, 斑点噪声, 图像细节, 多目标优化, 聚类

Abstract: SAR images change detection is a challenging task in the field of remote sensing,and it is an urgent problem to keep trade-off between robustness to noise and effectiveness of preserving the details.However,in order to better suppress speckle noise,it is inevitable that most of change detection methods loss image details to some extent.In order to solve this problem,a multi-objective clustering algorithm based on MOEA/D is proposed for change detection in SAR images.The change detection problem is formulated as a multi-objective optimization problem.Two conflicting objectives are constructed and then optimized by the proposed multi-objective clustering algorithm simultaneously.Finally,we obtain a set of change detection maps by the proposed technique.And the users can choose an appropriate one to satisfy their requirements.Experimental results on two SAR images show that the proposed method works well.

Key words: SAR images, Change detection, Speckle noise, Image details, Multi-objective optimization, Clustering

中图分类号: 

  • TP391.4
[1]RADKE R J,ANDRA S,Al-KOFAHI O,et al.Image change detection algorithms:a systematic survey[J].IEEE Transactions on Image Processing,2005,14(3):294-307.
[2]BRUZZONE L,PRIETO D F.An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images[J].IEEE Transactions on Image Processing,2002,11(4):452-466.
[3]BAZI Y,BRUZZONE L,MELGANI F.An unsupervised ap-proach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images[J].IEEE Tran-sactions on Geoscience and Remote Sensing,2005,43(4):874-887.
[4]BOVOLO F,BRUZZONE L.A detail-preserving scale-drivenapproach to change detection in multitemporal SAR images[J].IEEE Transactions on Geo-science and Remote Sensing,2005,43(12):2963-2972.
[5]BUJOR F,TROUVE E,VALET L,et al.Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images[J].IEEE Transactions on Geo-science and Remote Sensing,2004,42(10):2073-2084.
[6]GONG M,ZHOU Z,MA J.Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering[J].IEEE Transactions on Image Processing,2011,21(4):2141-2151.
[7]HAO M,SHI W,ZHANG H,et al.Unsupervised change detec-tion with expectation-maximization-based level set[J].IEEE Geoscience and Remote Sensing Letters,2013,11(1):210-214.
[8]GONG M,ZHAO S,JIAO L,et al.A novel coarse-to-finescheme for automatic image registration based on SIFT and mutual information[J].IEEE Transactions on Geoscience and Remote Sensing,2013,52(7):4328-4338.
[9]SINGH A.Review article digital change detection techniquesusing remotely-sensed data[J].International Journal of Remote Sensing,1989,10(6):989-1003.
[10]GONG M,SU L,JIA M,et al.Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images[J].IEEE Transactions on Fuzzy Systems,2013,22(1):98-109.
[11]LI H,GONG M,LIU J.A local statistical fuzzy active contour model for change detection[J].IEEE Geoscience and Remote Sensing Letters,2014,12(3):582-586.
[12]MA A,ZHONG Y,ZHANG L.Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(8):4202-4217.
[13]ZHANG Q,LI H.MOEA/D:A multiobjective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[14]SHIM V A,TAN K C,TANG H.Adaptive memetic computing for evolutionary multiobjective optimization[J].IEEE Transactions on Cybernetics,2014,45(4):610-621.
[15]WANG J,REN W,ZHANG Z,et al.A hybrid multiobjective memetic algorithm for multiobjective periodic vehicle routing problem with time windows[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,50(11):4732-4745.
[16]DEB K,AGRAWAL S,PRATAP A,et al.A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-II[C]//Parallel Problem Solving from Nature PPSN VI:6th International Conference.Paris,France,2000:849-858.
[17]BOVOLO F,CAMPS-VALLS G,BRUZZONE L.A supportvector domain method for change detection in multitemporal images[J].Pattern Recognition Letters,2010,31(10):1148-1154.
[18]CELIK T.Unsupervised change detection in satellite imagesusing principal component analysis and k-means clustering[J].IEEE Geoscience and Remote Sensing Letters,2009,6(4):772-776.
[19]GHOSH A,MISHRA N S,GHOSH S.Fuzzy clustering algo-rithms for unsupervised change detection in remote sensing images[J].Information Sciences,2011,181(4):699-715.
[20]MACQUEEN J.Some methods for classification and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability.1967:281-297.
[21]PRICE K,STORN R M,LAMPINEN J A.Differential evolu-tion:a practical approach to global optimization[M].Springer Science & Business Media,2006:37-131.
[22]LI H,ZHANG Q.Multiobjective optimization problems withcomplicated Pareto sets,MOEA/D and NSGA-II[J].IEEE Transactions on Evolutionary Computation,2008,13(2):284-302.
[23]ROSIN P L,IOANNIDIS E.Evaluation of global image thres-holding for change detection[J].Pattern recRgnition Letters,2003,24(14):2345-2356.
[24]HAO M,TAN M,ZHANG H.A change detection frameworkby fusing threshold and clustering methods for optical medium resolution remote sensing images[J].European Journal of Remote Sensing,2019,52(1):96-106.
[25]XIE F,QIAO W,GAO T,et al.Fuzzyactive contour model with markov random field for change detection[J].IEEE Access,2022,10:77406-77417.
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