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Land-Use/Land-Cover Classification Using Elephant Herding Algorithm

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Abstract

In recent years, swarm intelligence algorithms such as particle swarm optimisation, ant colony optimisation, cuckoo search and artificial bee colony algorithm have shown promising results in multispectral image classification. Elephant herding algorithm is one of the newly emerging nature inspired algorithms which can analyse multispectral pixels and determine the information of class via fitness function. When the spectral resolution of the satellite imagery is increased, the higher within-class variability reduces the statistical separability between the LU/LC classes in spectral space and tends to continue diminishing classification accuracy of the traditional classifiers. These are mostly per pixel and parametric in nature. Experimental result has revealed that elephant herding algorithm shows an improvement of 10.7% on Arsikere taluk and 6.63% on NITK campus over support vector machine.

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Acknowledgements

The author graciously thanks Dr. Dwarkish G S Professor, Hydraulics Department, NITK, Mangalore, for providing the remote sensed data for this study.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka, 570 016, India

    J. Jayanth

  2. Department of Electronics and Communication Engineering, ATME College of Engineering, Mysore, Karnataka, 570 028, India

    V. S. Shalini

  3. SDM Institute of Technology, Ujire, Belthangady, 574240, India

    T. Ashok Kumar

  4. Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan, Karnataka, 573 202, India

    Shivaprakash Koliwad

Authors
  1. J. Jayanth
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  2. V. S. Shalini
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  3. T. Ashok Kumar
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  4. Shivaprakash Koliwad
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Correspondence to J. Jayanth.

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Jayanth, J., Shalini, V.S., Ashok Kumar, T. et al. Land-Use/Land-Cover Classification Using Elephant Herding Algorithm. J Indian Soc Remote Sens 47, 223–232 (2019). https://doi.org/10.1007/s12524-018-00935-x

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  • DOI: https://doi.org/10.1007/s12524-018-00935-x

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