Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative Approach

Authors

  • Dr. Anil Kumar Gupta  HOD, Department of Computer Science & Applications, Barkatullah University, Bhopal, Madhya Pradesh, India
  • Dibya Jyoti Bora  Senior IT Faculty, School of Computing Sciences, Kaziranga University, Jorhat , Assam, India
  • Fayaz Ahmad Khan   Guest Faculty, Department of Computer Science & Applications, Barkatullah University, Bhopal, Madhya Pradesh, India

Keywords:

Color Image Segmentation, CLAHE, Median filter, Multispectral satellite image, RGB, HSV Color Space, FCM Algorithm

Abstract

Multispectral satellite color images need special treatment for object-based classification like segmentation. Traditional algorithms are not efficient enough for performing segmentation of such high-resolution images. So, an innovative approach for segmentation of multispectral color images is proposed in this paper. The proposed approach consists of two phases. In the first phase, the preprocessing of the selected bands is taken place for noise removal and contrast enhancement. In the second phase, fuzzy segmentation of the enhanced version obtained in the first phase is carried out. The results found are quite promising and comparatively better than the other state of the art algorithms.

References

  1. Ding, J. Sun and Y. Zhang, "FCM Image Segmentation Algorithm Based on Color Space and Spatial Information", International Journal of Computer and Communication Engineering, pp. 48-51, 2013.
  2. Gonzalez and R. Woods, Digital image processing. Reading, Massachusetts: Addison-Wesley Publishing Company, 2007.
  3. J. Bora, A.K. Gupta, "Clustering Approach Towards Image Segmentation: An Analytical Study", International Journal of Research in Computer Applications and Robotics, ISSN 2320-7345, Vol.2, Issue.7, Pg.: 115-124 July 2014
  4. Chris Solomon, Toby Breckon, "Fundamentals of Digital Image Processing", ISBN 978 0 470 84472 4.
  5. K. Gupta, D.J. Bora, "A Novel Color Image Segmentation Approach Based On K-Means Clustering with Proper Determination of the Number of Clusters and Suitable Distance Metric", International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 7 No. 09 Sep 2016, pp. 395-409.
  6. Abbas and M. Rydh, "Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm", International Journal of Image, Graphics and Signal Processing, vol. 4, no. 10, pp. 48-53, 2012.
  7. Byun, Y. Han and T. Chae, "A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery", KSCE Journal of Civil Engineering, vol. 17, no. 2, pp. 486-497, 2013.
  8. J. Bora, "Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study", Indian J.Sci.Res. 15 (1): 115-131, 2017.
  9. J. Bora, "Performance Comparison of K-Means Algorithm and FCM Algorithm with Respect to Color Image Segmentation", International Journal of Emerging Technology and Advanced Engineering, Volume 7, Issue 8, August 2017, pp. 460-470.
  10. Ganesan P and V. Rajini, "YIQ color space based satellite image segmentation using modified FCM clustering and histogram equalization", 2014 International Conference on Advances in Electrical Engineering (ICAEE), 2014.
  11. Rahman, "An automatic color textured image segmentation algorithm using mean histogram features", International Conference on Electrical & Computer Engineering (ICECE 2010), 2010.
  12. Kalist, P. Ganesan, B. Sathish, J. Jenitha and K. Basha.shaik, "Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space", Procedia Computer Science, vol. 57, pp. 49-56, 2015.
  13. S. Baboo, S. Thirunavukkarasu, "Image Segmentation using High Resolution Multispectral Satellite Imagery implemented by FCM Clustering Techniques", International Journal of Computer Science Issues (IJCSI) 11.3 (2014): 154-160.
  14. . Koschan, M. Abidi, "Digital Color Image Processing",Wiley-Interscience New York, NY, USA ©2008,ISBN:0470147083 9780470147085.
  15. J. Bora, "AERSCIEA: An Efficient and Robust Satellite Color Image Enhancement Approach", Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, ACSIS, Vol. 10, pp.3–13 (2017), DOI: http://dx.doi.org/10.15439/2017R53.
  16. European Space Agency (2009, October 23) Earth from Space: ‘Land of Terror.’ Accessed. December 1, 2017.
  17. visibleearth.nasa.gov. (2017). Landsat Image Gallery - Bold Beauty in the Tanezrouft Basin. online] Available at: https://landsat.visibleearth.nasa.gov/view.php?id=91349 Accessed 2 Dec. 2017].
  18. C. Bezdek ,"Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York, 1981.
  19. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 1973, 32-57.
  20. J. Bora, A.K. Gupta, "Impact of Exponent Parameter Value for the Partition Matrix on the Performance of Fuzzy C Means Algorithm", International Journal of Scientific Research in Computer Science Applications and Management Studies, Volume 3, Issue 3 (May 2014).
  21. Wang, A.Bovik, (2009). Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Processing Magazine, 26(1), 98-117. doi:10.1109/msp.2008.930649
  22. 22"Z.Wang, A.Bovik, , H.Sheikh, , E.Simoncelli, (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.819861

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Anil Kumar Gupta, Dibya Jyoti Bora, Fayaz Ahmad Khan , " Multispectral Satellite Color Image Segmentation Using Fuzzy Based Innovative Approach, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.968-975, January-February-2018.