Abstract
Speckle noise is a main obstacle for change detection tasks of synthetic aperture radar (SAR) images. However, change detection methods often focus on removing noise and ignore the importance of preserving details of SAR images, which results in a loss of classification accuracy. In order to alleviate the contradiction between removing noise and preserving details, a multi-objective change detection method based on a modified Gaussian mixture model (GMM) is proposed in our paper. In the framework of our multi-objective model, one objective is composed of an special GMM of being applied to distinguish changed and unchanged regions and preserve details. The another is a carefully crafted noise reduction operator for removing speckle noise, which is constructed as a penalty to refine the result of classification. The aforementioned conflicting objectives are optimized simultaneously, which is conducted by a common multi-objective evolutionary algorithm based on decomposition (MOEA/D). Then, a series of solutions are obtained, all of which represent a balance of detail preservation and noise reduction. Compared with three classical change detection methods, the proposed method is applied to four real datasets in experimental studies. Experimental results and theoretical analysis reveal its effectiveness.
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Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans Med Imaging 21(3):193–199
Atasever U, Kesikoglu M, Ozkan C (2016) A new artificial intelligence optimization method for pca based unsupervised change detection of remote sensing image data. Neural Netw World 26(2):141–154
Bianco V, Memmolo P, Leo M, Montresor S, Distante C, Paturzo M, Picart P, Javidi B, Ferraro P (2018) Strategies for reducing speckle noise in digital holography. Light: Science & Applications 7(1):48
Celik T (2010) Image change detection using gaussian mixture model and genetic algorithm. J Visual Commun Image Representation 21(8):965–974
Chang PC, Chen SH, Zhang Q, Lin JL (2008) Moea/d for flowshop scheduling problems. In: 2008 IEEE congress on evolutionary computation (IEEE World Congress on Computational Intelligence), IEEE, pp 1433–1438
Chen S, Zhang D (2004) Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(4):1907–1916
Gong G, Zhang H, Yao M (2015) Speckle noise reduction algorithm with total variation regularization in optical coherence tomography. Opt Express 23(19):24699–24712
Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151
Gong M, Su L, Jia M, Chen W (2014) Fuzzy clustering with a modified mrf energy function for change detection in synthetic aperture radar images. IEEE Trans Fuzzy Syst 22(1):98–109
Gong M, Zhao J, Liu J, Miao Q, Jiao L (2016) Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans Neural Netw Learning Syst 27(1):125–138
Guan C, Yuen KKF, Coenen F (2019) Particle swarm optimized density-based clustering and classification: supervised and unsupervised learning approaches. Swarm Evol Comput 44:876–896
Jia L, Li M, Zhang P, Wu Y, Zhu H (2016) Sar image change detection based on multiple kernel k-means clustering with local-neighborhood information. IEEE Geosci Remote Sens Lett 13(6):856–860
Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. IEEE Trans Evol Comput 13(2):284–302
Li H, Gong M, Wang Q, Liu J, Su L (2016) A multiobjective fuzzy clustering method for change detection in sar images. Appl Soft Comput 46:767–777
Li HC, Celik T, Longbotham N, Emery WJ (2015) Gabor feature based unsupervised change detection of multitemporal sar images based on two-level clustering. IEEE Geosci Remote Sens Lett 12(12):2458–2462
Liu J, Gong M, Miao Q, Su L, Li H (2015) Change detection in synthetic aperture radar images based on unsupervised artificial immune systems. Appl Soft Comput 34:151–163
Ma J, Jiang J, Liu C, Li Y (2017) Feature guided gaussian mixture model with semi-supervised em and local geometric constraint for retinal image registration. Inform Sci Inform Comput Sci Intell Syst Appl 417:128–142
Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm Evol Comput 33:1–17
Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I, Papathanassiou KP (2013) A tutorial on synthetic aperture radar. IEEE Geosci Remote Sens Mag 1(1):6–43
Opara KR, Arabas J (2019) Differential evolution: a survey of theoretical analyses. Swarm Evol Comput 44:546–558
Pan S, Shi W, He P, Ming H, Zhang X (2016) Novel approach to unsupervised change detection based on a robust semi-supervised fcm clustering algorithm. Remote Sens 8(3):264
Pandeeswari B, Sutha J, Parvathy M (2020) A novel synthetic aperture radar image change detection system using radial basis function-based deep convolutional neural network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02091-y
Reynolds D (2015) Gaussian mixture models. Encyclopedia of biometrics pp 827–832
Schlaffer S, Matgen P, Hollaus M, Wagner W (2015) Flood detection from multi-temporal sar data using harmonic analysis and change detection. Int J Appl Earth Obs Geoinform 38:15–24
Scrucca L, Fop M, Murphy TB, Raftery AE (2016) mclust 5: clustering, classification and density estimation using gaussian finite mixture models. The R Journal 8(1):289–317
Tewkesbury AP, Comber AJ, Tate NJ, Lamb A, Fisher PF (2015) A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens Environ 160:1–14
Vellasques E, Sabourin R, Granger E (2012) Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems. In: Proceedings of the 14th annual conference on Genetic and evolutionary computation, ACM, pp 73–80
Wei G, Lv Z, Ming H (2017) Change detection method for remote sensing images based on an improved markov random field. Multimedia Tools Appl 76(17):17719–17734
Xu G, Cui Q, Shi X, Ge H, Zhan ZH, Lee HP, Liang Y, Tai R, Wu C (2019) Particle swarm optimization based on dimensional learning strategy. Swarm Evol Comput 45:33–51
Yang J, Fan J, Ai D, Wang X, Zheng Y, Tang S, Wang Y (2016) Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing 195:88–95
Zhang Q, Li H (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhang Q, Liu W, Li H (2009) The performance of a new version of moea/d on cec09 unconstrained mop test instances. In: 2009 IEEE congress on evolutionary computation, IEEE, pp 203–208
Zheng Y, Jiao L, Liu H, Zhang X, Hou B, Wang S (2017) Unsupervised saliency-guided sar image change detection. Pattern Recognit 61:309–326
Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49
Acknowledgements
This work was supported in part by National Natural Science Foundation of China (Grant No. 62076204), the National Natural Science Foundation of Shaanxi Province under Grantnos. 2018JQ6003 and 2018JQ6030, the China Postdoctoral Science Foundation (Grantnos. 2017M613204 and 2017M623246), the Fundamental Research Funds for the Central Universities, and the seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University.
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Authors Xiaodong Liu, Jiao Shi, Yu Lei, Shiying Wang and Lina Huo declare that they have no conflict of interest.
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This work was supported by Shenzhen Research and Development Foundation (Grant no.JCYJ20170306153943097), the China Postdoctoral Science Foundation (Grant no. 2017M613204) and the National Natural Science Foundation of China (GrantNo.61603299 and 61602385).
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Shi, J., Liu, X., Yang, S. et al. An initialization friendly Gaussian mixture model based multi-objective clustering method for SAR images change detection. J Ambient Intell Human Comput 14, 15161–15173 (2023). https://doi.org/10.1007/s12652-020-02584-w
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DOI: https://doi.org/10.1007/s12652-020-02584-w