MASCA: Multiscale Adaptive Spectral Component Analysis for Hyperspectral Image Classification

In this letter, we propose multiscale adaptive spectral component analysis (MASCA), a frequency-domain preprocessing method that adaptively identifies discriminative discrete cosine transform (DCT) components through multiscale peak analysis. MASCA assigns a relevance score to each DCT component by detecting magnitude transitions across multiple scales while integrating spatial coherence and spectral stability constraints, with hyperparameters tuned via Bayesian optimization. Experimental results on four benchmark hyperspectral datasets (Indian Pines, Pavia University, Botswana, and Salinas) demonstrate the effectiveness of the proposed method.