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

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 a...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2019-02, Vol.47 (2), p.223-232
Main Authors: Jayanth, J., Shalini, V. S., Ashok Kumar, T., Koliwad, Shivaprakash
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Classification
Earth and Environmental Science
Earth Sciences
Fitness
Herding
Image classification
Land cover
Land use
Pixels
Remote Sensing/Photogrammetry
Research Article
Satellite imagery
Satellites
Search algorithms
Spectral resolution
Support vector machines
Swarm intelligence
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Summary: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.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-018-00935-x