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Change detection method for remote sensing images based on an improved Markov random field

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Abstract

The fixed weights between the center pixel and neighboring pixels are used in the traditional Markov random field for change detection, which will easily cause the overuse of spatial neighborhood information. Besides the traditional label field cannot accurately identify the spatial relations between neighborhood pixels. To solve these problems, this study proposes a change detection method based on an improved MRF. Linear weights are designed for dividing unchanged, uncertain and changed pixels of the difference image, and spatial attraction model is introduced to refine the spatial neighborhood relations, which aims to enhance the accuracy of spatial information in MRF. The experimental results indicate that the proposed method can effectively enhance the accuracy of change detection.

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Acknowledgments

Research reported in this paper was supported by the Natural Science Foundation of China (No. 51304199); the Open Projects of “State Key Laboratory of Coal Resources and Safe Mining, CUMT” (No.SKLCRSM13X08);the Fundamental Research Funds for the Central Universities (NO. 2014XT01).

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this article.

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Authors and Affiliations

  1. State Key Laboratory of Coal Resources and Mine Safety, School of Mines, China University of Mining & Technology, Xuzhou, 221116, China

    Wei Gu

  2. Chinese Academy of Science, SIAT, Shenzhen, China

    Zhihan Lv

  3. School of Environment and Spatial Informatics, China University of Mining & Technology, Xuzhou, 221116, China

    Ming Hao

Authors
  1. Wei Gu
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  2. Zhihan Lv
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  3. Ming Hao
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Correspondence to Wei Gu.

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Gu, W., Lv, Z. & Hao, M. Change detection method for remote sensing images based on an improved Markov random field. Multimed Tools Appl 76, 17719–17734 (2017). https://doi.org/10.1007/s11042-015-2960-3

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  • DOI: https://doi.org/10.1007/s11042-015-2960-3

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