Skip to main content
SpringerLink
Log in

The data analysis of roughness extraction of target topography using minimum median plane fitting method

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

According to the problem that the elevation data does not reflect the slope and surface roughness of target topography, the preprocessing of topographic elevation data and extraction algorithm of topographic feature are proposed, and the corresponding extraction of topographic feature is done. A terrain risk assessment method is presented based on terrain roughness and slope information fusion, aiming at the problem that terrain roughness and gradient cannot be directly reflected from the terrain elevation data, in this paper. The innovation is that it is the first time that the bilinear interpolation algorithm is applied in preprocess of elevation data and extraction of topographic feature, as well as the terrain roughness and gradient information fusion algorithm are applied to terrain feature extraction and risk assessment for the first time. By simulation and checking calculation of a certain digital topography example, it is proved that the extraction method of topographic information based on elevation data is feasible and reliable. It will provide a new research approach for target information recognition and topography risk assessment accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Bay, H., Ess, A., Tuytelaars, T., SURF.: Speeded up robust features [C]. In: GRAZ: European Conference on Computer Vision, pp. 404–417 (2006)

  2. Yang, F., Wei, L., Zhang, Z.: Image mosaic based on phase correlation and harris operator [J]. J. Comput. Inf. Syst. 8(6), 2647–2655 (2012)

    Google Scholar 

  3. Yanhui, S., Kunyu, T., Jian, W.: A new seeded region growing algorithm for large object segmentation [C]. In: BMEI: International Conference on Biomedical Engineering and Informatics, pp. 47–51 (2011)

  4. Lang, X., Zhu, F., Hao, Y.: Fast two-dimensional Otsu algorithm based on integral image [J]. Chin. J. Sci. Instrum. 30(1), 39–43 (2009)

    Google Scholar 

  5. Raizer, V.: Multisensor data fusion for advanced ocean remote sensing studies [C]. In: Geoscience and Remote Sensing Symposium (IGARSS), pp. 1622–1625. Melbourne, VIC (2013)

  6. Li, X., Liu, T., Deng, S.: Fast recognition of hand vein with SURF descriptors [J]. Chin. J. Sci. Instrum. 32(4), 831–836 (2011)

    Google Scholar 

  7. Yu, H., Jin, W., Liu, X.: An objective assessment method of digital image mosaic artifacts visibility based on visual perception [J]. Acta Armament. 31(9), 1209–1215 (2010)

    Google Scholar 

  8. Owen, J.P., Ziv, E., Bukshpun, P., et al.: Test-retest reliability of computational network measurements derived from the structural connectome of the human brain [J]. Brain Connect 3, 160–176 (2012)

    Article  Google Scholar 

  9. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations [J]. Neuroimage 10, 1059–1069 (2009)

    Google Scholar 

  10. Salvador, R., Peña, A., Menon, D. K.: Formal characterization and extension of the linearized diffusion tensor model. Hum [J]. Brain Mapp. 24, 144–155 (2007)

  11. Schwarz, A.J., McGonigle, J.: Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage 55, 1132–1146 (2010)

    Article  Google Scholar 

  12. Tustison, N.J., Avants, B.B., Cook, P.A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  13. Landman, B.A., Huang, A.J., Gifford, A.: Multi-parametric neuroimaging reproducibility: a 3-t resource study. Neuroimage 54, 2854–2866 (2010)

    Article  Google Scholar 

  14. Bassett, D.S., Brown, J.A., Deshpande, V.: Conserved and variable architecture of human white matter connectivity. Neuroimage 54, 1262–1279 (2010)

    Article  Google Scholar 

  15. Xue, R., Van Zijl, P.C., Crain, B.J.: In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging. Magn. Reson. Med. 42, 1123–1127 (2006)

    Article  Google Scholar 

  16. Zalesky, A., Fornito, A., Harding, I.H.: Whole-brain anatomical networks: does the choice of nodes matter. Neuroimage 50, 1201–1215 (2009)

    Google Scholar 

  17. Sumathi, C.S., Kumar, A.V.S.: Neural network based plant identification using leaf characteristics fusion. Int. J. Comput. Appl. 89(5), 31–35 (2014)

    Google Scholar 

  18. Hartley, R.: Kruppa’s. equations derived from the fundamental matrix. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 133–135 (2007)

    Article  Google Scholar 

  19. Mahamud, S., Hebert, M., Omori, Y et al.: Provably-convergent iterative methods for projective structure from motion. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1018–1025 (2010)

  20. Pollefeys, M., Van Gool, L.J., Vergauwen, M., et al.: Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)

    Article  Google Scholar 

  21. Gwo, C.Y., Wei, C.H.: Plant identification through images: using feature extraction of key points on leaf contours. Appl. Plant Sci. 1(11), 1–9 (2013)

  22. Luo, N., Sun, Q., Geng, L.: An extended SURF descriptor and its application in remote sensing images registration. Acta Geod. et Cartogr. Sin. 42(3), 383–388 (2013)

    Google Scholar 

  23. Cheng, Y., Xue, D., Han, X.: Fast image mosaic based on wavelet transform for remote sensing. J. Northeast. Univ. (Natural Sci.) 29(10), 1385–1388 (2008)

  24. Yang, B.: Image classification by latent topic model with dirichletian processes. J. Comput. Inf. Syst. 7(11), 3803–3810 (2011)

    Google Scholar 

  25. Pollefeys, M., Van Gool, L.J., Vergauwen, M., et al.: Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)

    Article  Google Scholar 

  26. Li, H., Chen, H.: A visualization method for multi-relation in dataset. Clust. Comput. 20(1), 225–237 (2017)

    Article  Google Scholar 

  27. Zhang, X.M., He, G.J.: Spectral-spatial multi-feature classification of remote sensing big data based on a random forest classifier for land cover mapping. Clust. Comput. 20(3), 2311–2321 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by Project from education department of Shaanxi Province (No. 16JK1366), president fund from Xi’an Technological University (No. XAGDXJJ1404), Key Problem Tackling Project of Shaanxi Scientific and Technological Office (No. 2016GY-024) and non-traditional machining Key Laboratory Project of Shaanxi Province (No. 15JS041).

Author information

Authors and Affiliations

  1. College of Electromechanical, Xi’an Technological University, Xi’an, 710021, China

    Qiangfeng Wang, Yan Cao, Yu Bai, Yujia Wu & Qingyun Wu

Authors
  1. Qiangfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yujia Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingyun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiangfeng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Cao, Y., Bai, Y. et al. The data analysis of roughness extraction of target topography using minimum median plane fitting method. Cluster Comput 22 (Suppl 4), 10385–10395 (2019). https://doi.org/10.1007/s10586-017-1582-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1582-0

Keywords

Search

Navigation