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Self-similarity Based Multi-layer DEM Image Up-Sampling

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 965))

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Abstract

As one of the basic data of GIS, DEM data which expresses the surface elevation data is widely used in many fields. How to obtain a wide range of high-precision elevation data is a big challenge, the simple interpolation algorithm currently used is less accurate. Due to the fractal data characteristics of terrain data, DEM data shows strong self-similarity. Based on this feature, this paper proposes a multi-layer Dem image up-sampling method. Image up-sampling is performed multiple times in layers on the low-resolution DEM image, therefore, high-precision DEM information with less error is obtained. In this paper, elevation data of 30 m is expanded to elevation data of 10 m by gradually using this method. Experimental results show that the algorithm can achieve good results and has a small deviation from the real elevation data of 10 m.

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References

  1. Ebrahimi, M., Vrscay, E.R.: Solving the inverse problem of image zooming using “self-examples”. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 117–130. Springer, Heidelberg (2007)

    Google Scholar 

  2. Mandelbrot, B.B.: The Fractal Geometry of Nature, vol. 51, p. 286 (1983). ISBN: 0-7167-1186-9

    Article  Google Scholar 

  3. Mandelbrot, B.B.: Stochastic models for the Earth’s relief, the shape and the fractal dimension of the coastlines, and the number-area rule for Islands. Proc. Natl. Acad. Sci. U. S. A. 72, 3825–3828 (1975)

    Article  MathSciNet  Google Scholar 

  4. Mandelbrot, B.: How long is the coast of Britain? Statistical self-similarity and fractional dimension. Sci. 156, 636 (1967)

    Article  Google Scholar 

  5. Lathrop Jr., R.G., Peterson, D.L.: Identifying structural self-similarity in mountainous landscapes. J. Landsc. Ecol. 6, 233–238 (1992)

    Article  Google Scholar 

  6. Boming, Y.U., Jianhua, L.I.: Fractal dimensions for unsaturated porous media. Fractals 12, 17–22 (2004)

    Article  Google Scholar 

  7. Jin, Y., Li, X., Zhao, M., Liu, X., Li, H.: A mathematical model of fluid flow in tight porous media based on fractal assumptions. Int. J. Heat Mass Transf. 108, 1078–1088 (2017)

    Article  Google Scholar 

  8. Rouphael, T.J., Cruz, J.R.: A spatial interpolation algorithm for the upsampling of uniform linear arrays. IEEE Trans. Signal Processing 47, 1765–1769 (1999)

    Article  Google Scholar 

  9. Kirkland, E.J.: Bilinear interpolation. In: Advanced Computing in Electron Microscopy, vol. 4, pp. 103–115. Springer, Boston (2010)

    Chapter  Google Scholar 

  10. Yu, L., Lin, W., Guangheng, N., Zhentao, C.: Spatial distribution characteristics of irrigation water requirement for main crops in China. Trans. CSAE. 25, 6–12 (2009)

    Google Scholar 

  11. Woodard, R.: Interpolation of spatial data: some theory for Kriging. Technometrics 42, 2 (2000)

    Article  Google Scholar 

  12. Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. In: IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56–65. March–April (2002)

    Google Scholar 

  13. Xin, Z., Chenlei, L., Qian, Y., Ping, G.: A patch analysis based repairing method for two dimensional fiber spectrum image. Opt. Int. J. Light. Electron Opt. 157, 1186–1193 (2018)

    Article  Google Scholar 

  14. Jin, Y., Zhu, Y.B., Li, X.: Scaling invariant effects on the permeability of fractal porous media. J. Transp. in Porous Media. 109, 433–453 (2015)

    Article  Google Scholar 

Download references

Acknowledgments

The research work described in this paper was fully supported by the National Key R&D program of China (2017YFC1502505) and the grant from the National Natural Science Foundation of China (61472043). Qian Yin is the author to whom all correspondence should be addressed.

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Correspondence to Qian Yin .

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Zheng, X., Chen, Z., Han, Q., Deng, X., Sun, X., Yin, Q. (2020). Self-similarity Based Multi-layer DEM Image Up-Sampling. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_53

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  • DOI: https://doi.org/10.1007/978-3-030-20454-9_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20453-2

  • Online ISBN: 978-3-030-20454-9

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