Skip to main content
SpringerLink
Log in

Efficient shadow detection by using PSO segmentation and region-based boundary detection technique

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The presence of shadows in satellite images is inevitable, and hence, shadow detection and removal has become very essential. In this paper, a shadow detection algorithm based on PSO has been used to identify shadows in very high-resolution satellite images. The image is first preprocessed using a bilateral filter to eliminate the noise followed by which PSO-based shadow segmentation is used to segment the shadow regions. Canny edge detection is done to identify the edges of the objects in the image. The results of the edge detection and segmentation are combined using a logical operator to generate the final shadow segmented image with well-defined boundaries. The accuracy is validated using precision and recall parameters.

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

Similar content being viewed by others

References

  1. Usha Nandini D, Leni ES (2014) A survey of the existing shadow detection techniques. In: International Conference on Control, Instrumentation, Communication and Computational Technologies, pp 175–177

  2. Tsai VJD (2006) A comparative study on shadow compensation of color aeriel images invariant color models. IEEE Trans Geosci Remote Sens 44(6):1661–1671

    Article  Google Scholar 

  3. Matens D, Baesens B, Fawcett T (2011) Editorial survey: swarm intelligence for data mining. Mach Learn 82(1):1–42

    Article  MathSciNet  Google Scholar 

  4. Javed O, Shah M (2002) Tracking and object classification for automated surveillance. In: Proceedings of European Conference on Computer Vision, vol 4, pp 343–357

  5. Leone A, Distante C (2007) Shadow detection of moving objects based on texture analysis. Pattern Recogn 40(4):1222–1233

    Article  MATH  Google Scholar 

  6. Tian YL, Lu M, Hampapu A (2005) Robust and efficient foreground analysis for real-time videa surveillance. In: IEEE Conference on Computer Vision and Pattern Recognition, vol 1, pp 1182–1187

  7. Salvador E, Cavallaro A, Eebrahimi T (2001) Shadoe identification and classification use invariant color models. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1545–1548

  8. Gonzalez RC, Woods RE (1992) Digital image processing. Addison-Wesley, Reading

    Google Scholar 

  9. Fang LZ, Qiong WY, Sheng YZ (2008) A method to segment moving vehicle cast shadow based on wavelet transform. Pattern Recogn Lett 29(16):2182–2188

    Article  Google Scholar 

  10. Huang J, Xie W, Tang L (2004) Detection of and compensation for shadows in colored urban aerial images. In: 5th World Congress, Intelligent Control and Automation, Hangzhou, China, Jun 2004, pp 3098–3100

  11. Polidorio AM, Flores FC, Imai NN, Tommaselli AMG, Franco C (2003) Automatic shadow segmentation in aerial color images. In: Proceedings XVI Brazilian Symposium on Computer graphics and Image Processing, Oct 2003, pp 270–277

  12. Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 8(1):62–69

    Article  Google Scholar 

  13. Makarau Aliaksei, Ritcher Rudolf, Muller Rupert, Reinartz Peter (2011) Adaptive shadow detection using a blackbody radiator model. IEEE Trans Geosci Remote Sens 49(6):2049–2059

    Article  Google Scholar 

  14. Fornarelli Girolamo, Giaquinto Antonio (2013) An unsupervised multi-swarm clustering technique for image segmentation. Swarm Evol Comput 11:31–45

    Article  Google Scholar 

  15. Kennedy J, Eberhart R Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948

  16. Premalatha K, Natarajan AM (2009) Discrete PSO with GA operators for document clustering. Int J Recent Trends Eng 1(1):20–24

    Google Scholar 

  17. Omran MGH, Aved Salman AE, Engelbrecht AP (2005) Dynamic clustering using particle swarm optimization with application in image segmentation. J Theor Adv 8:332–344

    MathSciNet  Google Scholar 

  18. Dhalia Sweetlin J, Nehemiah HK, Kannan A (2017) Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images, vol 145. Computer methods and programs in biomedicine. Elsevier, Amsterdam, pp 115–125

    Google Scholar 

  19. Ganapathy S, Sethukkarasi R, Yogesh P, Vijayakumar P, Kannan A (2014) An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization. Sadhana 39(2):283–302

    Article  MathSciNet  MATH  Google Scholar 

  20. Nithya R, Muthu Priya K, Vigneshwari S (2017) Stitching large images by enhancing SURF and RANSAC algorithm. In: 2017 Second IEEE International Conference on Electrical, Computer and Communication Technologies, 22–24 Feb 2017

  21. Selvi M, Logambigai R, Ganapathy S, Khanna Nehemiah H, Arputharaj K (2017) An intelligent agent and fso based efficient routing algorithm for wireless sensor network. In: 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM). IEEE, pp 100–105

  22. Tomasai C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of IEEE International Conference on Computer Vision, Bombay, India

  23. Albert Mayan J, Ravi T (2014) Optimized regression testing using genetic algorithm and dependency structure matrix. Int J Appl Eng Res 9(20):7679–7690

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    D. Usha Nandini

  2. SRR Engineering College, Chennai, India

    Ezil Sam Leni

Authors
  1. D. Usha Nandini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ezil Sam Leni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Usha Nandini.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Usha Nandini, D., Leni, E.S. Efficient shadow detection by using PSO segmentation and region-based boundary detection technique. J Supercomput 75, 3522–3533 (2019). https://doi.org/10.1007/s11227-018-2292-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2292-y

Keywords

Search

Navigation