Multimedia Tools and Applications

, Volume 78, Issue 1, pp 947–964 | Cite as

Road extraction using modified dark channel prior and neighborhood FCM in foggy aerial images

  • Wang Fengping
  • Wang WeixingEmail author


It is still a challenge to accomplish road extraction from the aerial images in foggy weather. In this paper, a road extraction method based on modified dark channel prior and improved neighborhood FCM (Fuzzy C-means) is proposed for foggy aerial images. Firstly, a defogging method based on modified dark channel prior is applied to increase the image contrast and highlight the road areas. In the proposed method, a region filtering function is designed to generate the dark channel image, and an adaptive parameter is set up to adjust the defogging degree automatically. Secondly, an improved neighborhood FCM algorithm is studied to extract the road area. According to the neighborhood feature, the spatial distance and the gray level difference can be calculated as the parameters of the objective function which can eliminate noise and promote the detection accuracy. Finally, an image post-processing procedure is utilized to connect the road gaps and remove the false road areas. The experimental results verify that the proposed method can achieve satisfied road extraction effect both on completeness and correctness.


Road extraction Foggy aerial image Dark channel prior Neighborhood fuzzy C-means 



The authors would like to thank the anonymous reviewers for their valuable remarks and suggestions. This work was supported by the Doctoral Dissertation Foster Fund of Chang’an University (Grant No. 310824165003), and the International Cooperation Project in China (Grant No. 2013KW03).


  1. 1.
    Barzohar M, Cooper DB (1996) Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Trans Pattern Anal Mach Intell 18(7):707–721CrossRefGoogle Scholar
  2. 2.
    Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefGoogle Scholar
  3. 3.
    Burt PJ, Kolczynski RJ (1993) Enhanced image capture through fusion, IEEE Computer on Computer Vision, Berlin, Germany: IEEE, p 173–182Google Scholar
  4. 4.
    Das S, Mirnalinee TT, Varghese K (2011) Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE Trans Geosci Remote Sens 49(10):3906–3931CrossRefGoogle Scholar
  5. 5.
    Ding L, Yao H, Guo HT, Liu ZQ (2015) Using neighborhood centroid voting to extract road centerline from high resolution image. J Image Graph 20(11):1526–1534Google Scholar
  6. 6.
    Ding Y, Xu Z, Zhang Y, Sun K (2017) Fast lane detection based on bird's eye view and improved random sample consensus algorithm. Multimed Tools Appl 76(21):22979–22998CrossRefGoogle Scholar
  7. 7.
    Duan ZG, Li Y, Wang E, et al (2016) Road and navigation line detection algorithm form shadow image based on the illumination invariant image[J]. Acta Opt Sin 36(12):206–213Google Scholar
  8. 8.
    Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–9CrossRefGoogle Scholar
  9. 9.
    Fu XY et al (2015) Road extraction from SAR images using tensor voting and snakes model. J Image Graph 20(10):1403–1411Google Scholar
  10. 10.
    Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Trans Image Process 21(2):662–673MathSciNetCrossRefGoogle Scholar
  11. 11.
    Guo F, Cai ZX, Xie B, Tang J (2010) Review and prospect of image dehazing techniques. J Comput Appl 30(9):2417–2421Google Scholar
  12. 12.
    He Y, Wang H, Zhang B (2004) Color-based road detection in urban traffic scenes. IEEE Trans Intell Transp Syst 5(4):309–318CrossRefGoogle Scholar
  13. 13.
    He KM, Sun J, Tang XO (2009) Single image haze removal using dark channel prior, IEEE conference on computer vision and pattern recognition, New York, USA, 1956–1963Google Scholar
  14. 14.
    Li G, Wu JF, Lei ZY (2014) Research progress of image haze grade evaluation and dehazing technology. Lasernal 35(9):1–6Google Scholar
  15. 15.
    Ma RG, Wang WX, Liu S (2012) Extracting roads based on Retinex and improved canny operator with shape criteria in vague and unevenly. J Appl Remote Sens 6(23):1–14Google Scholar
  16. 16.
    Marikhu R, Dailey M, Makhanov S, Honda K (2007) A family of quadratic snakes for road extraction. In Proc Asian Conf Comput Vis, p 85–94Google Scholar
  17. 17.
    McCartney EJ (1975) Optics of the atmosphere: scattering by molecules and particles. Wiley, HobokenGoogle Scholar
  18. 18.
    Miao ZL, Wang B (2014) A semi-automatic method for road centerline extraction from VHR images. IEEE Geosci Remote Sens Lett 11(11):1856–1860CrossRefGoogle Scholar
  19. 19.
    Mukundan R, Ramakrishnan KR (1998) Moment functions in image analysis theory and applications. World Scientific, SingaporeCrossRefGoogle Scholar
  20. 20.
    Nayar SK, Narasimhan SG (1999) Vision in bad weather, IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, p 820–827Google Scholar
  21. 21.
    Rizvandi NB, Pizurica A, Philips W, Ochoa D (2008) Edge linking based method to detect and separate individual elegans worms in culture. In: Proc. DICTA, p 65–70Google Scholar
  22. 22.
    Shang ZM et al (2014) Aerial image clustering analysis based on genetic fuzzy C-means algorithm and Gabor-gist descriptor, 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery, p 77–81Google Scholar
  23. 23.
    Song M, Civco D (2004) Road extraction using SVM and image segmentation. Photogramm Eng Remote Sens 70(12):1365–1371CrossRefGoogle Scholar
  24. 24.
    Trinder J, Wang Y (1998) Knowledge-based road interpretation in aerial images. Int Arch Photogramm Remote Sens 32(4):635–640Google Scholar
  25. 25.
    Wang WX, Liu S (2015) Online burning material pile detection on color clustering and quaternion based edge detection in boiler. KSII Trans Internet Inf Syst 9(1):190–207MathSciNetGoogle Scholar
  26. 26.
    Wang L et al (2012) Ship detection of remote sensing image on FRHD and multi-points curvature based polygon approximation. J Appl Sci Eng Technol 4(15):2590–2599Google Scholar
  27. 27.
    Wang WX et al (2016) Rock fracture image acquisition using two kinds of lighting and fusion on a wavelet transform. Bull Eng Geol Environ 75:311–324CrossRefGoogle Scholar
  28. 28.
    Wang WX et al (2016) A review of road extraction from remote sensing image, journal of traffic and transportation. Engineering 3(3):271–282Google Scholar
  29. 29.
    Xiao SB, Li Y (2015) Fast multiscale Retinex algorithm of image haze removal with color fidelity. Comput Eng Appl 51(6):176–180Google Scholar
  30. 30.
    Ye J, Ding Y (2018) Controllable keyword search scheme supporting multiple users. Future Gener Comput Syst 81:433–442CrossRefGoogle Scholar
  31. 31.
    Zhan B, Wu Y, Ji S (2010) Infrared image enhancement method based on stationary wavelet transformation and Retinex. Acta Opt Sin 30(10):2788–2793CrossRefGoogle Scholar
  32. 32.
    Zhang XG et al (2014) A dehazing method in single image based on double-area filter and image fusion. Acta Automat Sin 40(8):1733–1739zbMATHGoogle Scholar
  33. 33.
    Zhu C, Shi W, Pesaresi M, Liu L, Chen X, King B (2005) The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics. Int J Remote Sens 26(24):5493–5508CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information EngineeringChang’an UniversityXi’anPeople’s Republic of China

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