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Commonly used spectral preprocessing methods include SG smoothing (for random noise removal), SNV transformation (to correct scattering-induced spectral distortions), and MSC (which eliminates spectral differences due to scattering intensity variations and enhances spectrumreference data correlations) ( 2.7 Feature selection algorithms The large sample size and numerous spectral bands in hyperspectral data often lead to data redundancy and high dimensionality, which can result in a decline in both the accuracy and efficiency of spectral model classification