Skip to main content
SpringerLink
Log in

Feature extraction for man-made objects segmentation in aerial images

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper describes a new aerial image segmentation algorithm by level set. The non-subsampled contourlet transform (NSCT) is selected to represent the image features for the classification of man-made object. NSCT can avoid pseudo-Gibbs phenomena around singularities during the process of image denoising, owing to the properties of shift-invariant. NSCT also enriches the set of basis functions which makes it possible to extract some critical signal features. The optimization of basis selection is applied in the NSCT to ensure the decomposition based on the maximum information content. Another kind of feature named RMSerror is also extracted from fBm model. Then a modified Mumford-Shah model which comprises the NSCT features and RMSerror features extracted from the aerial images is built to segment the aerial image by a necessary level set evolution. At last the proposed method is proven to be effective by the results of experiments.

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.

Similar content being viewed by others

References

  1. Li J., Najmi A. and Gray R.M. (2000). Image classification by a two-dimensional hidden Markov model. IEEE Trans. Signal Process. 48(2): 517–533

    Article  Google Scholar 

  2. Reno, A.L., Booth, D.M.: Using models to recognise man-made objects, Visual Surveillance. Second IEEE Workshop on, 26 June 1999 pp. 33 – 40 (1999)

  3. Solka J.L., Marchette D.J. and Wallet B.C. (1998). Identification of man-made regions in unmanned aerial vehicle imagery and videos. IEEE Trans. PAMI 20(8): 852–857

    Google Scholar 

  4. Carlotto M.J. (1996). Detecting man-made features in SAR imagery. Geosci. Remote Sens. Symp. 1: 27–31

    Google Scholar 

  5. Lebitt, S., Aghdasi, F.: Texture measures for building recognition in aerial photographs. Commun. Signal Proces. COMSIG ’97. In: Proceedings of the 1997 South African Symposium on, 9–10 September (1997)

  6. Haala N. and Brenner C. (1998). Interpretation of urban surface models using 2D building information. Comput. Vis. Image Underst. 72(2): 204–214

    Article  Google Scholar 

  7. Candès., E.J.: Ridgelets: theory and applications. USA: Department of Statistics, Stanford University (1998)

  8. Do, M.N.:Contourlets and sparse image expansions. In: Proceedings of SPIE—The International Society for Optical Engineering, v 5207, n 2, pp. 560–570

  9. Candès, E.J.: Monoscale ridgelets for the representation of images with edges. USA: Department of Statistics, Stanford University (1999)

  10. Donoho, D.L., Duncan, M.R.: Digital curvelet transform: strategy, implementation and experiments. In: Proceedings of Aerosense 2000, Wavelet Applications VII. SPIE 4056. pp. 12 – 29 (2000)

  11. Coifman, R.R, Donoho, D.L.: Translation-invariant de-noising http://www.citeseer.ist.psu.edu/coifman95translationinvariant. html

  12. Eslami, R., Radha, H.: The contourlet transform for image de-noising using cycle spinning conference record of the thirty-seventh Asilomar conference on signals, Systems and Computers, pp. 1982–1986 (2003)

  13. Cunha A.L., Zhou J. and Do M.N. (2006). The nonsubsampled contourlet transform: theory,design, and applications. IEEE Trans. Image Process 15(10): 3089–3101

    Article  Google Scholar 

  14. da Cunha, A.L., Zhou, L., Do, M.N.: Nonsubsampled contourlet transform: filter design and applications in denoising 2005. In: International Conference on Image Processing: I-749–52 (2006)

  15. Zhou, J., Cunha, A.L., Do, M.N.: Nonsubsampled contourlet transform: construction and application in enhancement 2005. In: International Conference on Image Processing: I-469–72 (2006)

  16. Daubechies I. (1988). Time-frequency localization operators: a geometric phase space approach. IEEE Trans. Inf. Theory 34: 605–612

    Article  MATH  MathSciNet  Google Scholar 

  17. Coifman R.R. and Wickerhauser M.V. (1992). Entropy-based algorithms for best-basis selection. IEEE Trans. Inf Theory 38: 713–718

    Article  MATH  Google Scholar 

  18. Mallat S. and Zhang Z. (1993). Matching Pursuit in a time-frequency dictionary. IEEE Trans. Signal Process. 41: 3397–3415

    Article  MATH  Google Scholar 

  19. Chen S.S., Donoho D.L. and Saunders M.A. (1998). Atomic decomposition by basis pursuit SIAM. J. Sci. Comput. 20: 33–61

    MathSciNet  Google Scholar 

  20. Cooper B.E., Chenoweth D.L. and Selvage J.E. (1994). Fractal error for detecting man-made features in aerial images. Electron. Lett. 30(7): 554–555

    Article  Google Scholar 

  21. Guo, C., Xin, Y., Zhihong, M.: A two-stage level set evolution scheme for man-made objects detection in aerial images. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. pp. 474–479 (2005)

  22. Mumford D. and Shah J. (1989). Optimal approximation by piece wise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42(5): 577–685

    Article  MATH  MathSciNet  Google Scholar 

  23. Sethian O.S. and Fronts J.A. (1988). Propagating with curvature- dependent speed: algorithms based on Hamilon-Jacobi formulations. J. Comput. Phys. 79: 12–49

    Article  MATH  MathSciNet  Google Scholar 

  24. Osher S. and Paragios N. (2003). Geometric level set methods in imaging, vision and graphics. Spinger, Newyork

    Book  MATH  Google Scholar 

  25. Sethian J.A (1999). Level set methods and fast marching methods: evolving interfaces in computational geometry. Fluid mechanics, computer vision and materials science, 2nd ed.. Cambridge University Press, Cambridge

    Google Scholar 

  26. Chan F.T. and Vese L. (2001). Active contours without edges. IEEE Trans. Image Process. 10(2): 266–277

    Article  MATH  Google Scholar 

  27. Aujol J.-F., Aubert G. and Blanc-Féraud L. (2003). Wavelet-based level set evolution for classification of textured images. IEEE Trans. Image Process. 12(12): 1634–1641

    Article  MathSciNet  Google Scholar 

  28. Matalon, B.,Elad, M., Zibulevsky, M.: Improved denoising of images using modelling of a redundant contourlet transform. In:Proceedings of SPIE—The International Society for Optical Engineering, vol. 5914. Wavelets XI, pp. 1–12 (2005)

Download references

Author information

Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, 200240, People’s Republic of China

    Wang Wei & Yang Xin

Authors
  1. Wang Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wang Wei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, W., Xin, Y. Feature extraction for man-made objects segmentation in aerial images. Machine Vision and Applications 19, 57–64 (2008). https://doi.org/10.1007/s00138-007-0080-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-007-0080-4

Keywords

Search

Navigation