Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

  • Authors

    • Rincy Merlin Mathew
    • S. Purushothaman
    • P. Rajeswari
    2017-12-21
    https://doi.org/10.14419/ijet.v7i1.1.9286
  • Particle Swarm Optimization, Echostate Neural Network, Genetic Algorithm, Vegetation Segmentation.

  • Abstract

    This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

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  • How to Cite

    Merlin Mathew, R., Purushothaman, S., & Rajeswari, P. (2017). Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation. International Journal of Engineering & Technology, 7(1.1), 184-188. https://doi.org/10.14419/ijet.v7i1.1.9286

    Received date: 2018-01-26

    Accepted date: 2018-01-26

    Published date: 2017-12-21