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

Author

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  • A. L. Choodarathnakara
  • Jayanth J
  • Shivaprakash Koliwad
  • C. G. Patil
  • Srikrishnashastri C

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.

Suggested Citation

  • A. L. Choodarathnakara & Jayanth J & Shivaprakash Koliwad & C. G. Patil & Srikrishnashastri C, 2018. "Investigating Misclassification of Semi-urban LU/ LC Features on IRS Data based on Fuzzy K-mean," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(3), pages 1305-1319, April.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i3:id:hcseit1833474
    Note: Article URL: https://ijsrcseit.com/CSEIT1833474
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