Home » Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images

Abhishek Mishra

Dept. of ECE, Scope College of Engineering, Bhopal, India

email: setu17687@gmail.com

Bharti Chaurasia

Dept. of  ECE, Scope College of Engineering, Bhopal, India

email: bharti.chourasia27@gmail.com

Yashwant Kurmi

Dept. of ECE, Maulana Azad National Institute of Technology Bhopal, India



The article for the specific region segmentation is discussed here. This paper compares the work done on the contour model based shadow detection algorithm with the random field model based shadow detection method. The contouring based shadow detection model adapts the traditional geometric active contours that the contour is steadily subjective toward segmenting the shadow region and the dark regions in the image.  The shadow region segmentation follows the post processing for optimal threshold calculation.  The complexity metric is constructed to separate the boundary of true shadow regions. Whereas the random field model based shadow detection method uses a superficial surface descriptor, and colour shade, to describe the colour surface variants. The conditional random field is combined with the colour descriptors to find illumination pairs and obtain the shadow regions. The shadow region riddance by local colour constancy uses anisotropic diffusion to estimate the local image pixel illumination in shadow region and provides the better performance in shadow region illumination enhancement.


Shadow detection; Shadow removal; Aerial images; Conditional random field, Contour detection

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Cited as

Abhishek Mishra, Bharti Chaurasia and Yashwant Kurmi, “Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images,” International Journal of Advanced Engineering and Management, vol. 2, no. 4, pp. 86-89,  2017.


  1. M.I. Elbakary and K. M. Iftekharuddin, “Shadow Detection of Man-Made Buildings in High-Resolution Panchromatic Satellite Images,” IEEE Trans. on Geoscience and Remote Sensing, vol. 52, no. 9, Sept. 2014, pp.5374-5386.
  2. X. Yuan, M. Ebner, Z.Wang, “Single-image Shadow Detection and Removal Using Local Colour Constancy Computation,” IET Image Process., 2015, Vol. 9, Iss. 2, pp. 118–126, doi: 10.1049/iet-ipr.2014.0242.
  3. J. Zhu, K.G.G. Samuel, S. Masood, and M.F. Tappen, “Learning to Recognize Shadows in Monochromatic Natural Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
  4. J.-F. Lalonde, A.A. Efros, and S.G. Narasimhan, “Estimating natural illumination from a single outdoor image,” Proc. 12th IEEE Int’l Conf. Computer Vision, 2009.
  5. K. Karsch, V. Hedau, D. Forsyth, and D. Hoiem, “Rendering Synthetic Objects Into Legacy Photographs,” Proc. ACM Siggraph, 2011.
  6. D.L. Waltz, “Generating Semantic Descriptions From Drawings of Scenes with Shadows,” Technical Report, 1972.
  7. H. Barrow and J. Tenenbaum, “Recovering Intrinsic Scene Characteristics From Images,” Computer Vision Systems, pp. 3-26, 1978.
  8. D. Sharma, Y. Kurmi, and V. Chaurasia, “Formation of super- Resolution Image: A Review,” Int. Jour. of Emerging Tech. and Adv. Engg., Apr. 2014, vol. 4, no. 4, pp. 218-221.
  9. Y. Kurmi and V. Chaurasia, “An Image Fusion Approach Based on Adaptive Fuzzy Logic Model with Local Level Processing,” Int. Jour. of Comp. Appl., Aug. 2015, vol. 124, no.1, pp. 39-42.
  10. S. Tiwari, K. Chauhan, and Y. Kurmi “Shadow detection and Compensation in Aerial Images Using MATLAB,” Int. Jour. of Comp. Appl., June 2015, vol. 119, no.20, pp. 5-9.
  11. Y. Kurmi and V. Chaurasia, “Performance of Haze Removal Filter for Hazy and Noisy Images,” Int. Jour. of Sci. Engg. and Tech., Apr. 2014, vol. 3 no. 4, pp. 437-439.
  12. M. K. Patle, B. Chourasia, and Y. Kurmi, “High Dynamic Range Image Analysis Through Various Tone Mapping Techniques,” Int. Jour. of Comp. Appl., vol.153, no. 11, Nov. 2016 pp. 14-17.
  13. A. Kumar, B. Chourasia, and Y. Kurmi, “Image Defogging by Multiscale Depth Fusion and Hybrid Scattering Model,” International Journal of Computer Applications, vol. 155, no 11, Dec. 2016, pp. 34-38.
  14. J. Liu, T. Fang, and D. Li, “Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection,” IEEE Trans. on Geo. and Remote Sens., vol. 49, no. 12, Dec. 2011, pp.5092-5103.
  15. T. F. Chan and L. A. Vese, “Active Contours Without Edges,” IEEE Trans. Image Process., vol. 10, no. 2, pp. 266–277, Feb. 2001.
  16. H. K. Zhao, T. Chan, B. Merriman, and S. Osher, “Variational Level Set Method Approach to Multiphase Motion,” J. Comput. Phys., vol. 127, no. 1, pp. 179–195, Aug. 1996.
  17. C. Fredembach, G. Finlayson, ‘Hamiltonian Path-Based Shadow Removal’. Proc. on 16th British Machine Vision Conf., Oxford, UK, September 2005, pp. 24, no. 5, pp. 603–619.
  18. R. Guo, Q. Dai and D. Hoiem, Paired Regions for Shadow Detection and Removal, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 35, No. 12, December 2013.


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