Investigating Misclassification of Semi-urban LU/ LC Features on IRS Data based on Fuzzy K-mean

Authors

  • A. L. Choodarathnakara  Assistant Professor, Dept. of Electronics & Communication Engineering, Government Engineering College, Kushalnagar, Karnataka, India
  • Dr. Jayanth J  Associate Professor, Dept. of E&C Engineering, GSSSIETW, Mysore,
  • Dr. Shivaprakash Koliwad  Emeritus Professor, Dept. of Electronics & Communication Engineering, Malnad College of Engineering, Hassan, Karnataka, India
  • Dr. C. G. Patil  Director (R), Dept. of Space (ISRO), Master Control Facility (MCF), Hassan, Karnataka, India
  • Srikrishnashastri C  Assistant Professor, Dept. of Electronics & Communication Engineering, VCET, Puttur, Karnataka, India

Keywords:

Remote Sensing, Semi-urban Area, Mixed Pixels, ISODATA, Fuzzy K-Mean.

Abstract

Semi-urban area is a dynamic functioning of land use as ‘divide’ between city and countryside (the urban fringe theory). The area under investigation is the Arasikere Semi-urban Area, located at 44km North of Hassan District, Karnataka State, INDIA with an elevation of approximately 806 m (2,644 ft) Above Mean Sea Level and is known for its coconut production. The satellite data are of multispectral image of IRS-P6 and panchromatic image of IRS-P5 satellites launched and maintained by the Indian Space Research Organization. Since all the three bands of IRS image are correlated, all bands must be filtered carefully until no correlation is present. Hard classification techniques were applied with ISODATA followed by Fuzzy K-mean unsupervised classifiers on Arasikere semi-urban area and found that hard classifiers failed to classify semi-urban area since the study area is characterized with mixed classes. Semi-urban area is difficult to be classified when “Hard Classification” is used but is good tool for homogeneous area where no mixed pixels exist.

References

  1. Ashok Kumar, T., 2010. "Advanced Image Processing Techniques and Algorithms For Classification of Semi-Urban Land Use/Land Cover Features Using High Resolution Satellite Data", Ph.D. Thesis, Visvesvaraya Technological University, Karnataka State, INDIA.
  2. Bardossy, A., and Samaniego, L., 2002. "Fuzzy Rule-Based Classification of Remotely Sensed Imagery". IEEE Transactions on Geoscience and Remote Sensing, 40 (2): 362-374.
  3. Bezdek, J. C., James Keller, Raghu Krishnapuram, and Pal, N. R., 2005. "Fuzzy Models and Algorithms for Pattern Recognition and Image Processing". Library of Congress Cataloging-in-Publication Data.
  4. Lu, D., and Weng, Q., 2006. "Use of Impervious Surface in Urban Land-Use Classification", Remote Sensing of Environment, Elsevier, 102: 146-160.
  5. Lu, D., and Weng, Q., 2007. "A Survey of Image Classification Methods and Techniques for Improving Classification Performance". International Journal of Remote Sensing, 28 (5): 823-870.
  6. Dinesh, M. S., Chidananda Gowda, K., and Nagabhushan, P., 1997. "Unsupervised Classification for Remotely Sensed Data using Fuzzy Set Theory", IEEE, 521-523.
  7. Duda, T., Canty, M., and Klaus, D., 1999. "Unsupervised Land-Use Classification of Multispectral Satellite Images: A Comparison of Conventional and Fuzzy-Logic Based Clustering Algorithms", IEEE, 1256-1258.
  8. Ehlers, M., Klonus, S., Astrand, P. J., and Rosso, P., 2010. "Multi-Sensor Image Fusion for Pansharpening in Remote Sensing", International Journal of Image and Data Fusion, Taylor & Francis, 1 (1): 25–45.
  9. ERDAS Field Guide, 1999. ERDAS Incorporation, Atlanta, Georgia, USA, 5th Edition.
  10. ERDAS IMAGINE Tour Guide,2001. ERDAS Incorporation, Atlanta, Georgia, USA.
  11. Foody, G. M., 2002. "Status of Land Cover Classification Accuracy Assessment, Remote Sensing of Environment", Elsevier Publications, 80: 185– 201.
  12. Gao, L., Pan, F., and Li, X., 2006. "A New Fuzzy Unsupervised Classification Method for Se Images", IEEE, 1706-1709.
  13. Ghosh, S., and Dubey, S. K., 2013. "Comparative Analysis of K-Means and Fuzzy C-Means Algorithms, International Journal of Advanced Computer Science and Applications, 4 (4): 35-39.
  14. Irvin, B. J., Ventura, S. J., and Slater, B. K., 1997. "Fuzzy and Isodata Classification of Landform Elements from Digital Terrain Data in Pleasant Valley, Wiscosin", 77:137-154.
  15. Khalid Imam Rahmani, M., Naina Pal and Kamiya Arora, 2014. "Clustering of Image Data using K-means and Fuzzy K-means", International Journal of Advanced Computer Science and Applications, 5 (7): 160-163.
  16. Khaleghi, B., Khamis, A., Karray, F. O., and Razavi, S. N., 2012. "Multisensor Data Fusion: A Review of the State-of-the-Art", Elsevier, 2-6.
  17. Manyara, C. G., and Lein, J. K., 1994. "Exploring the Suitability of Fuzzy Set Theory in Image Classification: A Comparative Study Applied to the Mau Forest Area Kenya", Proceedings of American Society for Photogrammetry and Remote Sensing, 384-391.
  18. Meeus, S. J., 2008. "Semi-Urban Areas in Landscape Research: A Review", Living Rev. Landscape Res., 2 (3): 1-45.
  19. Official Website of Hassan District, www.hassan.nic.in, maintained by Hassan District Administration & Designed by National Informatics Centre, Hassan District Centre, 1st Floor, Zilla Panchayat, Hassan.
  20. Pal, M., 2002. "Factors Influencing the Accuracy of Remote Sensing Classifications: A Comparative Study", Ph. D. Thesis, University of Nottingham. 
  21. Pohl, C., and Van Genderen, J. L., 1998. "Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications", International Journal of Remote Sensing, 19 (5): 823-854.
  22. Premkumar, P., and Anitha, J., 2011. "Accessing Relevant Images: Fuzzy K-Means", IEEE, 312-315.
  23. Sabin, M. J., 1987. "Convergence and Consistency of Fuzzy c-means/ISODATA Algorithms", IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (5): 661-668.
  24. Wang, B., Ono, A., Muramatsu, K., and Fujiwara, N., 1999. "Automated Detection and Removal of Clouds and their Shadows from Landsat-TM Images", IEICE Transactions on Information and System, E82-D (2): 453-460.
  25. Weng, Q., 2012. "Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods and Trends", Elsevier Publications, 117:34-49.
  26. Yang, M. S., 1993. "A Survey of Fuzzy Clustering", Pergamon Press Ltd, 18:1-16.

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

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Published

2018-04-30

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Volume 3, Issue 3, March-April-2018

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Research Articles

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[1]
A. L. Choodarathnakara, Dr. Jayanth J, Dr. Shivaprakash Koliwad, Dr. C. G. Patil, Srikrishnashastri C, " Investigating Misclassification of Semi-urban LU/ LC Features on IRS Data based on Fuzzy K-mean, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1305-1319, March-April-2018.