Land-Use/Land-Cover Classification Using Elephant Herding Algorithm
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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.
KeywordsSupport 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.
- Chen, S., & Tian, Y. (2015). Pyramid of spatial relations for scene-level land use classification. IEEE Transactions on Geoscience and Remote Sensing, 53(4), 947–1957.Google Scholar
- Gu, Y., Liu, T., Jia, X., Benediktsson, J. A., & Chanussot, J. (2016). Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for -hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3235–3247.CrossRefGoogle Scholar
- Jayanth, J., Ashok Kumar, T., & Koliwad, S. (2012). Comparative analysis of image fusion techniques for remote sensing. In Hassanien, A. E., Salem, A. M., Ramadan, R., & Kim, T. (Eds.), Proceedings of communications in computer and information science international conference on advanced machine learning technologies and applications (AMLTA 2012) Cairo, December 8–10 (Vol. 322, pp. 111–117). Berlin: Springer.Google Scholar
- Perronnin, F., Sãąnchez, J., Mensink, T. (2010). Improving the Fisher kernel for large-scale image classification. In European conference on computer vision (ECCV) (Vol. 6314, pp. 143–156). Berlin: Springer.Google Scholar