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A new approach toward object-based change detection

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Abstract

Object-based change detection has been the hotspot in remote sensing image processing. A new approach toward object-based change detection is proposed. The two different temporal images are unitedly segmented using the mean shift procedure to obtain corresponding objects. Then change detection is implemented based on the integration of corresponding objects’ intensity and texture differences. Experiments are conducted on both panchromatic images and multispectral images and the results show that the integrated measure is robust with respect to illumination changes and noise. Supplementary color detection is conducted to determine whether the color of the unchanged objects changes or not when dealing with multispectral images. Some verification work is carried out to show the accuracy of the proposed approach.

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References

  1. Singh A. Digital change detection techniques using remotely sensed data. Int J Remote Sens, 1989, 10: 989–1003

    Article  Google Scholar 

  2. Lu D, Mausel P, Brondizio E, et al. Change detection techniques. Int J Remote Sens, 2004, 25(12): 2365–2407

    Article  Google Scholar 

  3. Blaschke T, Strohl J. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GIS Zeitschrift fur Geoinformationssysteme, 2001, 6: 12–17

    Google Scholar 

  4. Gong J Y, Sui H G, Sun K M, et al. Object-level change detection based on full-scale image segmentation and its application to Wen-chuan Earthquake. Sci China Ser E-Tech Sci, 2008, 50(Suppl II): 110–122

    Article  Google Scholar 

  5. Desclee B, Bogaert P, Defourny P. Forest change detection by statistical object-based method. Remote Sens Environ, 2006, 102: 1–11

    Article  Google Scholar 

  6. Wang W J, Zhao Z M, Zhu H Q. Object-oriented change detection method based on multi-scale and multi-feature fusion. In: 2009 Urban Remote Sensing Joint Event, May 20–22, 2009, Shanghai, China

  7. Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE T Pattern Anal, 2002, 24(5): 603–619

    Article  Google Scholar 

  8. Li L, Leung M. Integrating intensity and texture differences for robust change detection. IEEE T Image Process, 2002, 11(2): 105–112

    Article  Google Scholar 

  9. IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology. IEEE Standard 610.4–1990, 1990

    Google Scholar 

  10. Reed T, Buf J. A review of recent texture segmentation and feature extraction techniques. CVGIP-Image Und, 1993, 57(3): 359–372

    Article  Google Scholar 

  11. Rosin P. Thresholding for change detection. Comput Vis Image Und, 2002, 86: 79–95

    Article  MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Guo Zhang & Yang Li

  2. Satellite of Surveying and Mapping Application center, State Bureau of Surveying and Mapping, Beijing, 100830, China

    Guo Zhang

  3. School of Printing and Packaging, Wuhan University, Wuhan, 430079, China

    ZhiJiang Li

Authors
  1. Guo Zhang
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  2. Yang Li
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  3. ZhiJiang Li
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Corresponding author

Correspondence to Guo Zhang.

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Zhang, G., Li, Y. & Li, Z. A new approach toward object-based change detection. Sci. China Technol. Sci. 53 (Suppl 1), 105–110 (2010). https://doi.org/10.1007/s11431-010-3215-1

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  • DOI: https://doi.org/10.1007/s11431-010-3215-1

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