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    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Pansharpening fuses high-resolution panchromatic and low-resolution multispectral images.
    • Regression-based methods commonly estimate injection coefficients at reduced resolution.

    Purpose of the Study:

    • To propose and analyze an iterative algorithm for estimating injection coefficients at full resolution for regression-based pansharpening.
    • To demonstrate the convergence and analytical calculation of the asymptotic value for the proposed method.

    Main Methods:

    • Development of an iterative algorithm for full-resolution injection coefficient estimation.
    • Analytical calculation of the asymptotic value reached by the iterative algorithm.
    • Performance evaluation using IKONOS and WorldView-3 sensor data at both reduced and full resolutions.

    Main Results:

    • Demonstrated convergence of the iterative algorithm for all practical cases, regardless of the initial guess.
    • Analytical calculation of the asymptotic value of the injection coefficients.
    • The proposed full-scale approach consistently outperformed state-of-the-art pansharpening methods in performance assessments.

    Conclusions:

    • The proposed full-resolution estimation of injection coefficients offers superior performance in regression-based pansharpening.
    • The developed iterative algorithm is robust and converges reliably.
    • This advancement provides a more effective approach for high-quality image fusion in remote sensing applications.