Land Use and Cover Mapping Using SVM and MLC Classifiers: A Case Study of Aurangabad City, Maharashtra, India
The fast developing cities like Aurangabad need an effective analysis of Land Use Land Cover. In this paper we examine the use of MLC and SVM for mapping the LULC from satellite Imagery. This paper investigates the accuracy of Support Vector Machine SVM and Maximum Likelihood Classification MLC for multi-spectral images of Aurangabad city and Waluj area. The satellite images collected from IRS-1C LISS III and PlanetScope Imagery. Our objective is to produce LULC map for Aurangabad and Waluj and estimate the change that happened to each class of the land cover. The results show that the increase of buildings area was significant from the period of 2008 to 2018. The accuracy of MLC was 94.13% and 85.65% with kappa 0.92, 0.81 in 2008 and 2018 respectively, and for SVM the accuracy was 93% with kappa 0.91, 94.4% with kappa 0.92 in 2008 and 2018 respectively. It was noticed that both SVM and MLC can be used effectively on LULC data analysis.
KeywordsRemote sensing Land Use Land Cover Maximum Likelihood Classifier Support Vector Machine GIS
I would Like to thank the DST-FIST with sanction no. SR/FST/ETI-340/2013 to the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University for supporting and funding this work. Also i would like to thank the university and department authorities for facilitating this work.
- 1.Giri, C.P.: Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press, Boca Raton (2012)Google Scholar
- 2.Jensen, J.R.: Remote Sensing of the Environment: An Earth Resource Perspective, 2nd edn. Pearson Education India, Koramangala (2009)Google Scholar
- 5.Ben-Hur, A., Weston, J. : A user’s guide to support vector machines. In: Data Mining Techniques for the Life Sciences, pp. 223–239 (2009)Google Scholar
- 8.Ding, H., Shi, J., Wang, Y., Wei, L.: An improved dark-object subtraction technique for atmospheric correction of Landsat 8. In: Proceeding of SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 98150K, 14 December 2015Google Scholar
- 9.Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective. Pearson Education Incorporated, Glenview (2016)Google Scholar
- 11.Verma, R.K., Kumari, K.S., Tiwary, R.K.: Application of remote sensing and GIS technique for efficient urban planning in India. In: Geomatrix Conference Proceedings (2009)Google Scholar
- 13.Gao, J.: Digital Analysis of Remotely Sensed Imagery. McGraw-Hill Professional, New York (2009)Google Scholar
- 17.Santosh, K.C., Wendling, L., Antani, S.K., Thoma, G.R.: Scalable arrow detection in biomedical images. In: ICPR 2014, pp. 3257–3262 (2014)Google Scholar