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

  • J. JayanthEmail author
  • V. S. Shalini
  • T. Ashok Kumar
  • Shivaprakash Koliwad
Research Article


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.


Support vector machine (SVM) Elephant herding (EH) Multispectral (MS) image classification 



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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Copyright information

© Indian Society of Remote Sensing 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringGSSS Institute of Engineering and Technology for WomenMysoreIndia
  2. 2.Department of Electronics and Communication EngineeringATME College of EngineeringMysoreIndia
  3. 3.SDM Institute of TechnologyUjire, BelthangadyIndia
  4. 4.Department of Electronics and Communication EngineeringMalnad College of EngineeringHassanIndia

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