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    Area of Science:

    • Spectroscopy and Spectroscopic Imaging
    • Optical Engineering
    • Data Harmonization Techniques

    Background:

    • Splicing correction is crucial for merging spectra from different sensors across overlapping ranges.
    • Existing methods may not fully address pixel-level noise in hyperspectral imaging.
    • Visible-near-infrared (VNIR) and short-wave infrared (SWIR) spectral ranges often require separate sensor capture.

    Purpose of the Study:

    • To extend splicing correction to reflectance imaging spectroscopy, specifically for VNIR and SWIR data.
    • To develop a method robust to pixel-level noise inherent in imaging spectroscopy.
    • To introduce an adaptive logistic function fitting approach for harmonizing spectral datasets.

    Main Methods:

    • Development of a novel splicing correction algorithm for hyperspectral imaging.
    • Utilizing adaptive fitting of a logistic function to calculate correction coefficients.
    • Application to VNIR and SWIR reflectance imaging spectroscopy data.

    Main Results:

    • The proposed method effectively harmonizes VNIR and SWIR spectral sets.
    • Demonstrated robustness against pixel-level noise sources.
    • Quantitative comparison against existing state-of-the-art splicing correction techniques.

    Conclusions:

    • The adaptive logistic fitting approach provides a viable solution for splicing correction in hyperspectral imaging.
    • The method enhances spectral data quality by mitigating noise and ensuring seamless spectral merging.
    • Usage conditions and performance benchmarks are provided for practical application.