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Detection in SAR Images Based on Histogram and Improved Elitist Genetic Fuzzy Clustering

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10603))

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Abstract

Change detection in Synthetic Aperture Radar Images has been an important technique for Synthetic Aperture Radar Images. In this paper, a novel unsupervised change detection algorithm based on histogram and improved elitist genetic fuzzy clustering is proposed. First, a difference image is generated by multiplying transform fusion. Second, we use the characteristics of the histogram to deal with the difference image. Then, the new algorithm is proposed to partition these characteristics into changed and unchanged regions. The proposed algorithm has the following merits: 1. FCM is employed to initialize the population and to calculate the fitness function of the genetic algorithm. 2. The optimal solution is selected by an elitist selection strategy based on population concentration and the optimal solution will be the initial clustering center of FCM, which significantly increases the convergence speed. 3. The histogram is utilized to reduce the sample points of images. Compared with the state-of-the-art algorithms, the experimental results demonstrate the effectiveness in processing of change detection in SAR images.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China, under Grants 61371201 and 61272279, the National Basic Research Program (973 Program) of China under Grant 2013CB329402.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an, 710071, Shaanxi, China

    Ronghua Shang, Weitong Zhang & Licheng Jiao

Authors
  1. Ronghua Shang
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  2. Weitong Zhang
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  3. Licheng Jiao
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Corresponding author

Correspondence to Ronghua Shang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Shang, R., Zhang, W., Jiao, L. (2017). Detection in SAR Images Based on Histogram and Improved Elitist Genetic Fuzzy Clustering. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-68542-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68541-0

  • Online ISBN: 978-3-319-68542-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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