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CRF-Based Simultaneous Segmentation and Classification of High-Resolution Satellite Images

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Global Changes and Natural Disaster Management: Geo-information Technologies

Abstract

Scale selection and uncertainty of image segmentation is still an intractable problem which influences the image classification results directly. To solve this problem, we adopt a CRF (Conditional Random Field)-based method to do segmentation and classification simultaneously. In this method, using probabilistic graphical model, we construct a three-level potential function which includes the pixels, the objects, and the link among the pixels and the objects to model their relations. We transform it to an optimization problem and use the graph cut algorithm to get the optimal solution. This method can refine the segmentation while getting good classification result. We do some experiments on the GF-1 high spatial resolution satellite images. The experiment results show that it is an effective way to improve the classification accuracy, avoid the boring segmentation scale and parameters selection and will highly improve the efficiency of image interpretation.

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Acknowledgements

The study was partially supported by the High-resolution Comprehensive Traffic Remote Sensing Application program under Grant No. 07-Y30B10-9001-14/16, the National Natural Science Foundation of China under Grant No. 41101410 and Foundation of Key Laboratory for National Geographic State Monitoring of National Administration of Survey, Mapping and Geoinformation under Grant No. 2014NGCM.

Author information

Authors and Affiliations

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Weihong Cui

  2. Collaborative Innovation Center for Geospatial Technology, Wuhan, China

    Weihong Cui

  3. Mobile Mapping Lab, University of Waterloo, Waterloo, Canada

    Weihong Cui & Jonathan Li

  4. China Highway Engineering Consulting Corporation, Beijing, China

    Guofeng Wang

  5. Xi’an University of Science and Technology, Xi’an, China

    Chenyi Feng & Yiwei Zheng

Authors
  1. Weihong Cui
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  2. Guofeng Wang
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  3. Chenyi Feng
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  4. Yiwei Zheng
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  5. Jonathan Li
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Corresponding author

Correspondence to Weihong Cui .

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Editors and Affiliations

  1. Institute of Disaster Management (WIDM) , Mississauga, Ontario, Canada

    Saied Pirasteh

  2. Department of Geography & Environmental Management, University of Waterloo, Waterloo, Ontario, Canada

    Jonathan Li

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Cui, W., Wang, G., Feng, C., Zheng, Y., Li, J. (2017). CRF-Based Simultaneous Segmentation and Classification of High-Resolution Satellite Images. In: Pirasteh, S., Li, J. (eds) Global Changes and Natural Disaster Management: Geo-information Technologies . Springer, Cham. https://doi.org/10.1007/978-3-319-51844-2_3

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