Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images

Authors

  • Abbas TAATI Department of Soil Science Engineering, University of Tehran, Karaj
  • Fereydoon SARMADIAN Department of Soil Science Engineering, University of Tehran, Karaj
  • Amin MOUSAVI Department of Soil Science Engineering, University of Tehran, Karaj
  • Chamran Taghati Hossien POUR Department of Soil Science Engineering, University of Tehran, Karaj
  • Amir Hossein Esmaile SHAHIR Department of Soil Science Engineering, University of Tehran, Karaj

Keywords:

Remote sensing, satellite, overall accuracy, kappa coefficient

Abstract

Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing land use maps. The aim of this study is to create land use classification using a support vector machine (SVM) and maximum likelihood classifier (MLC) in Qazvin, Iran, by TM images of the Landsat 5 satellite. In the pre-processing stage, the necessary corrections were applied to the images. In order to evaluate the accuracy of the 2 algorithms, the overall accuracy and kappa coefficient were used. The evaluation results verified that the SVM algorithm with an overall accuracy of 86.67 % and a kappa coefficient of 0.82 has a higher accuracy than the MLC algorithm in land use mapping. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.

doi:10.14456/WJST.2015.33

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References

J Knorn, A Rabe, V Radeloff, T Kuemmerle, J Kozak and P Hostert. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sens. Environ. 2009; 113, 957-64.

R Borges, A Hernandez and L Nykjaer. Analysis of sea surface temperature time series of the south-eastern North Atlantic. Int. J. Remote Sens. 2004; 5, 869-91.

P Srivastava, D Han, M Rico-Ramirez, M Bray and T Islam. Selection of classification techniques for land use/land cover change investigation. Adv. Space Res. 2012; 50, 1250-65.

J Richards and X Jia. Remote Sensing Digital Image Analysis. 4th ed. Heidelberg, Springer, Germany, 2006, p. 359-88.

M Bray and D Han. Identification of support vector machines for runoff modelling. J. Hydroinform. 2004; 6, 265-80.

A Moghaddamnia, M Gosheh, M Nuraie, M Mansuri, D Han and E Schmitter. Performance evaluation of LLR, SVM,CGNN and BFGSNN models to evaporation estimation estimation. Water Geosci. 2010; 9, 108-13.

D Han, L Chan and N Zhu. Flood forecasting using support vector machines. J. Hydroinform. 2007; 9, 267-76.

N Vapnik and A Chervonenkis. On the uniform convergence of the relative frequencies of events to their probabilities. Theor. Probab. Appl. 1971; 16, 264-80.

N Vapnik. An overview of statistical learning theory. IEEE Trans. Neural Network. 1999; 10, 988-99.

B Dixon and N Candade. Multispectral land use classification using neural networks and support vector machines. Int. J. Remote Sens. 2008; 29, 1185-206.

G Foody and A Mathur. Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification. Remote Sens. Environ. 2004; 93, 107-17.

C Huang, LS Davis and JRG Townshend. An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 2002; 23, 725-49.

S Yousefi, M Tazeh, S Mirzaee, HR Moradi and SH Tavangar. Comparison of different classification algorithms in satellite imagery to produce land use maps (Case study: Noor city). J. Appl. RS GIS Tech. Nat. Res. Sci. 2011; 2, 15-25.

CJ Tucker, DM Grant and JD Dykstra. NASA’s global orthorectified landsat data set. Photogramm. Eng. Rem. Sens. 2004; 70, 313-22.

Erdas Inc. Erdas Field Guide. Erdas Inc., Atlanta, Georgia, 1999.

EE Osuna and R Freud. Support Vector Machines: Training and Applications. Massachusetts Institute of Technology Cambridge, USA, 1977, p. 1-144.

A Erener. Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection. Int. J. Appl. Earth Observ. Geoinform. 2013; 21, 397-408.

BP Manisha, G Chitra and N Umrikar. Image classification tool for land use/ land cover analysis: A comparative study of maximum likelihood and minimum distance method. Int. J. Geo. Earth. Environ. 2012; 6, 189-96.

RG Congalton. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ.1991; 37, 35-46.

D Lu, P Mausel, E Brondizio and E Moran. Change detection techniques. Int. J. Remote Sens. 2004; 25, 2365-407.

K Sundarakumar, M Harika, SK Aspita Begum, S Yamini and K Balakrishna. Land use and land cover change detection and urban sprawl analysis of Vijayawada city using multitemporal landsat data. Int. J. Eng. Sci. Tech. 2012; 4, 170-8.

JR Otukei and T Blaschke. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int. J. Appl. Earth Observ. Geoinform. 2010; 12, 527-31.

B Deilmai, B Ahmad and H Zabihi. Comparison of two classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia. IOP Conf. Ser. Earth Environ. Sci. 2014; 20, 1-6.

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Published

2014-12-01

How to Cite

TAATI, A., SARMADIAN, F., MOUSAVI, A., POUR, C. T. H., & SHAHIR, A. H. E. (2014). Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images. Walailak Journal of Science and Technology (WJST), 12(8), 681–687. Retrieved from https://wjst.wu.ac.th/index.php/wjst/article/view/1225