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

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.

Citation

Mu, C., Li, C., Liu, Y., Sun, M., Jiao, L., & Qu, R. (2017, June). Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm. Presented at 2017 IEEE Congress on Evolutionary Computation (CEC 2017), Donostia, Spain

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2017 IEEE Congress on Evolutionary Computation (CEC 2017)
• Start Date: Jun 5, 2017
• End Date: Jun 8, 2017
• Acceptance Date: Mar 5, 2017
• Online Publication Date: Jul 7, 2017
• Publication Date: 2017
• Deposit Date: Sep 19, 2017
• Publicly Available Date: Sep 19, 2017
• Peer Reviewed: Peer Reviewed
• Pages: 1150-1157
• Book Title: 2017 IEEE Congress on Evolutionary Computation (CEC) - Proceedings
• ISBN: 978-1-5090-4602-7
• 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
• Public URL: https://nottingham-repository.worktribe.com/output/871615
• Publisher URL: http://ieeexplore.ieee.org/abstract/document/7969436/
• 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
• Contract Date: Sep 19, 2017