Classification of Remote Sensing Image Based on Different Similarity Measures

  • Kartik Shah
  • Shantanu Santoki
  • Himanshu Ghetia
  • D. Aju
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Advanced wide field sensor (AWiFS) is a multi-spectral camera used to capture image from IRS-P6 (Indian remote sensing) satellite. Iterative self-organizing data analysis technique (ISODATA) is one of the most frequently used unsupervised classification algorithms. There are too many techniques available for classifying an image. In this paper, we will use similarity-based techniques to classify an image. Then, we will compare the result of each similarity measure classification techniques. We will use normalized difference vegetation index (NDVI) values to classify the images.


Unsupervised classification Similarity measures AWiFs image ISODATA 



The authors would like to thank the School of Computer Science and Engineering, VIT University, for giving them the opportunity to carry out this research.


  1. 1.
    E. Swinnen, F. Veroustraete, Extending the SPOT-VEGETATION time series (1998–2006) back in time with NOAA-AVHRR data (1985–1998) for Southern Africa. IEEE Trans. Geosci. Remote Sens. 46(2), 558–572 (2008)CrossRefGoogle Scholar
  2. 2.
    C.J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2), 127–150 (1979)CrossRefGoogle Scholar
  3. 3.
    C.O. Justice, E. Vermote, J.R.G. Townshend, R. Defries, D.P. Roy, D.K. Hall, V.V. Salomonson, J.L. Privette, G. Riggs, A. Strahler, W. Lucht, R.B. Myneni, Y. Knyazikhin, S.W. Running, R.R. Nemani, Z.M. Wan, A.R. Huete, W. van Leeuwen, R.E. Wolfe, L. Giglio, J.P. Muller, P. Lewis, M.J. Barnsley, The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36(4), 1228–1249 (1998)CrossRefGoogle Scholar
  4. 4.
    P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 40(8), 1824–1832 (2002)CrossRefGoogle Scholar
  5. 5.
    B.D. Wardlow, S.L. Egbert, J.H. Kastens, Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ. 108(3), 290–310 (2007)CrossRefGoogle Scholar
  6. 6.
    L.A. Méndez-Barroso, J. Garatuza-Payán, E.R. Vivoni, Quantifying water stress on wheat using remote sensing in the Yaqui Valley, Sonora. Mexico. Agric. Water Manag. 95(6), 725–736 (2008)CrossRefGoogle Scholar
  7. 7.
    A. Murni, A.K. Jain, J. Rais, framework for multi-date multisensor image interpretation. IEEE IGARSS. 3, 1851–1854 (1996)Google Scholar
  8. 8.
    P.R. Bajgiran, Y. Shimizu, F. Hosoi, K. Omasa, MODIS vegetation and water indices for drought assessment in semi-arid ecosystems of Iran. J. Agric. Meteorol. 65, 349–355 (2009)CrossRefGoogle Scholar
  9. 9.
    A.R. Huete, G. Ponce, Satellite observed shifts in seasonality and vegetation—rainfall relationships in the south-west USA. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38, 775–777 (2010)Google Scholar
  10. 10.
    D. Song, P. Guo, H. Sheng, Spatial distribution pattern of MODISNDVI and correlation between NDVI and meteorology factors in Shandong province in China. Piers Online 4, 191–196 (2008)CrossRefGoogle Scholar
  11. 11.
    A. Yuhas, L.A. Scuderi, MODIS-derived NDVI characterization of drought-induced evergreen dieoff in western North America. Geograph. Res. 47, 34–45 (2008)CrossRefGoogle Scholar
  12. 12.
    Y. Zheng, H. Qiu, in Mapping urban landuse types in Los Angeles using multi-date moderate-resolution imaging spectroradiometer vegetation image products. Proceedings of Second International Workshop on Earth Observation and Remote Sensing Applications (2012)Google Scholar
  13. 13.
    L. Prasad, S.S. Iyengar, High performance algorithms for object recoganization problem by multiresolution template matching (1995)Google Scholar
  14. 14.
    J.A. Mikhail, Faster image template matching in the sum of the absolute value of differences measure. IEEE Trans. Image Process. 10(4) (2001)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Kartik Shah
    • 1
  • Shantanu Santoki
    • 1
  • Himanshu Ghetia
    • 1
  • D. Aju
    • 1
  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia

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