Analysis, Classification, and Estimation of Pattern for Land of Aurangabad Region Using High-Resolution Satellite Image

  • Amol D. Vibhute
  • Rajesh K. Dhumal
  • Ajay D. Nagne
  • Yogesh D. Rajendra
  • K. V. Kale
  • S. C. Mehrotra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


Land use land cover (LULC) information extraction is a crucial exercise for agricultural land. The present study highlights the advantages of remote sensing, GIS, and GPS techniques for LULC mapping from high-resolution remote sensing data. High spatial resolution (5.8 m) satellite imagery of IRS-P6 Resourcesat-II LISS-IV having three spectral bands was utilized for LULC classification and for data processing ENVI 4.4 tool and Arc GIS10 software were used. Eight training samples for LULC classes have been selected from the image. Supervised classification using maximum likelihood (ML), Mahalanobis distance (MD), and minimum distance to means (MDM) were applied. The performances of above classifiers were evaluated in terms of the classification accuracy with respect to the collected real-time ground truth information. The evaluation result shows that the overall accuracies of LULC classifications are approximately 84.40, 77.98, and 74.31 % with Kappa coefficients 0.82, 0.74, and 0.70 for the ML, MD, and MDM, respectively. It is noticed that ML has a better accuracy than the MD and MDM classifiers and it is a more effective method for complex and noisy remote sensing data because of its unified approach for estimation of parameters.


Maximum likelihood classifier Land use/land cover Supervised classification Remote sensing 



Authors would like to acknowledge UGC for facility provided under UGC SAP (II) DRS Phase-I F.No.-3-42/2009 to Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. And financial assistance under UGC-BSR research fellowship.


  1. 1.
    Vibhute, A.D., Gawali, B.W.: Analysis and modeling of agricultural land use using remote sensing and geographic information system: a review. Int. J. Eng. Res. Appl. (IJERA) 3(3), 081–091 (2013)Google Scholar
  2. 2.
    Sadoun, B., Al Rawashdeh, S.: Applications of GIS and remote sensing techniques to land use management. In: IEEE/ACS International Conference on Computer Systems and Applications, pp. 233–237. IEEE (2009)Google Scholar
  3. 3.
    Zhang, Y., Chen, X., Su, S., Wu, J.: Making the best use of landsat MSS images for land use/cover change analysis. In: International Conference on Environmental Science and Information Application Technology. IEEE (2009)Google Scholar
  4. 4.
    Xiong, Y., Wang, R., Li, Z.: Extracting land use/cover of mountainous area from remote sensing images using artificial neural network and decision tree classifications. In: International Symposium on Intelligence Information Processing and Trusted Computing. IEEE (2010)Google Scholar
  5. 5.
    Kumar, V., Rai, S.R., Rathore, D.S.: Land use mapping of Kandi Belt of Jammu region. J. Indian Soc. Remote Sens. 32(4), 323–328 (2004)Google Scholar
  6. 6.
    Shamsudheen, M., Dasog, G.S., Tejaswini, N.B.: Land use/land cover mapping in the coastal area of North Karnataka using remote sensing data. J. Indian Soc. of Remote Sens. 33(2), 253–257 (2005)Google Scholar
  7. 7.
    Joshi, R.K., Rawat, G.S., Padaliya, H., Roy, P.S.: Land use/land cover identification in an Alpine and Arid region (Nubra Valley, Ladakh) using satellite remote sensing. J. Indian Soc. Remote Sens. 33(3), 371–380 (2005)Google Scholar
  8. 8.
    Lillesand, T.M., Kiefer, R.W., Chipman, J.W.: Remote Sensing and Image Interpretation, 6th edn. Wiley India Pvt. Ltd., New Delhi (2008)Google Scholar
  9. 9.
    Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E.: A land use and land cover classification system for use with remote sensor data. In: Geological Survey Professional Paper, vol. 964 (2001).
  10. 10., Maharashtra. Accessed 27 Sept 09 2014 08:03 p.m
  11. 11.
    Liu, J.G., Mason, P.J.: Essential Image Processing and GIS for Remote Sensing. Wiley, London (2009)Google Scholar
  12. 12.
    Gao, J.: Digital Analysis of Remotely Sensed Imagery. The McGraw-Hill Companies, Inc., New York (2009)Google Scholar
  13. 13.
    Al-Ahmadi, F.S., Hames, A.S.: Comparison of four classification methods to extract land use and land cover from Raw Satellite Images for some remote Arid Areas, Kingdom of Saudi Arabia. JKAU Earth Sci. 20(1), 167–191 (2009 A.D./1430 A.H.)Google Scholar
  14. 14.
    Sharma, S., Pradhan, R.: Classification methods for land use and land cover pattern analysis. Int. J. Innovative Technol. Explor. Eng. (IJITEE) 4(1) (2014). ISSN: 2278-3075Google Scholar
  15. 15.
    Vibhute, A.D., Nagne, A.D., Gawali, B.W., Mehrotra, S.C.: Comparative analysis of different supervised classification techniques for spatial land use/land cover pattern mapping using RS and GIS. Int. J. Sci. Eng. Res. 4(7) (2013). ISSN 2229-5518Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Amol D. Vibhute
    • 1
  • Rajesh K. Dhumal
    • 1
  • Ajay D. Nagne
    • 1
  • Yogesh D. Rajendra
    • 1
  • K. V. Kale
    • 1
  • S. C. Mehrotra
    • 1
  1. 1.Department of Computer Science & ITDr. B. A. M. UniversityMaharashtraIndia

Personalised recommendations