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Particle Swarm Optimization-Based Multispectral Image Fusion for Minimizing Spectral Loss

Author

Listed:
  • Patel, Abhishek
  • Anand, Rajesh

Abstract

A novel multispectral image fusion technique is proposed which minimizes the spectral loss of fused product using a proper objective function. It is found that the Relative Average Square Error (RASE) is a good choice to be considered as the objective function. A linear combination of multispectral bands is calculated in which the weights are optimized using particle swarm optimization algorithm. Several experimental studies have been conducted on three public domain datasets to show the effectiveness of the proposed approach in comparison with state-of-the-art methods. The objective and visual assessments of the proposed method support the claims provided in this paper.

Suggested Citation

  • Patel, Abhishek & Anand, Rajesh, 2019. "Particle Swarm Optimization-Based Multispectral Image Fusion for Minimizing Spectral Loss," MPRA Paper 94006, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:94006
    as

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    File URL: https://mpra.ub.uni-muenchen.de/94006/1/MPRA_paper_94006.pdf
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    References listed on IDEAS

    as
    1. Ashkaboosi, Maryam & Ashkaboosi, Farnoosh & Nourani, Seyed Mehdi, 2016. "The Interaction of Cybernetics and Contemporary Economic Graphic Art as "Interactive Graphics"," MPRA Paper 72717, University Library of Munich, Germany.
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    More about this item

    Keywords

    Pansharpening; particle swarm optimization; optimal weights; image fusion; panchromatic; multispectral.;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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