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A Cascaded Segmentation Method Based on Region Merging to Change Detection in Remote Sensing Images

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

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Abstract

Change detection based on image superpixels can extract more geomorphologic information among multitemporal remote sensing images than methods based on pixel difference. In this paper, we presented a cascaded segmentation method to extract clear change region boundry with noise supperssion. First, Simple linear iterative clustering (SLIC) is used to generate super pixels which adhere difference image boundries tightly for purpose of searching change regions. Second, one Statistical Region Merging (SRM) with dynamic sorting algorithm is modified to merge those homogeneous super pixels. After the candidate change regions established, classified change map are remerged by using simplified SRM. Finally, the proposed method are compared with methods based on PCA and MRF. Experimental results shows our method restrain the over segmentation and obtain better performance of change detection than conventional SRM algorithms.

N. Lv—Foundation item: National Natural Science Foundation of China (61571347, 61201293).

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Authors and Affiliations

  1. School of Electronic Engineering/VIP Lab., Xidian University, Taibai South Road 2, Xi’an, Shaanxi, China

    Ning Lv & Xinbo Gao

Authors
  1. Ning Lv
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  2. Xinbo Gao
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Corresponding author

Correspondence to Ning Lv .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Lv, N., Gao, X. (2017). A Cascaded Segmentation Method Based on Region Merging to Change Detection in Remote Sensing Images. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

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