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Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm

Mu, Caihong; Li, Chengzhou; Liu, Yi; Sun, Menghua; Jiao, Licheng; Qu, Rong

Authors

Caihong Mu

Chengzhou Li

Yi Liu

Menghua Sun

Licheng Jiao

Rong Qu



Abstract

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on the bi-temporal SAR images acquired in the same region. Then a saliency detection model is applied in the difference image to extract the salient regions containing the changed class pixels. The salient regions are further divided by fuzzy c-means (FCM) clustering algorithm into three categories: changed class (set of pixels with high gray values), unchanged class (set of pixels with low gray values) and undetermined class (set of pixels with middle gray value, which are difficult to classify). Finally, the proposed accelerated GA is applied to explore the reduced search space formed by the undetermined-class pixels according to an objective function considering neighborhood information. In S-aGA, an efficient mutation operator is designed by using the neighborhood information of undetermined-class pixels as the heuristic information to determine the mutation probability of each undetermined-class pixel adaptively, which accelerates the convergence of the GA significantly. The experimental results on two data sets demonstrate the efficiency of the proposed S-aGA. On the whole, S-aGA outperforms five other existing methods including the simple GA in terms of detection accuracy. In addition, S-aGA could obtain satisfying solution within limited generations, converging much faster than the simple GA.

Publication Date Jul 7, 2017
Peer Reviewed Peer Reviewed
APA6 Citation Mu, C., Li, C., Liu, Y., Sun, M., Jiao, L., & Qu, R. (2017). Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm. doi:10.1109/CEC.2017.7969436
DOI https://doi.org/10.1109/CEC.2017.7969436
Keywords change detection, Saliency map, fuzzy c-means (FCM), genetic algorithm (GA), Synthetic Aperture Radar (SAR) image
Publisher URL http://ieeexplore.ieee.org/abstract/document/7969436/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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