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Spectral decomposition, a pivotal process in hyperspectral imaging, involves separating mixed signals into their constituent parts, known as endmembers, to extract meaningful information. The Bayesian Information Criterion, a statistical metric derived from Bayesian probability theory, serves as a valuable tool for model selection in spectral decomposition reducing the risk of overfitting and enhancing the robustness of the unmixing analysis.
In this work we utilise BIC in spectral decomposition through fitting models with varying numbers of endmembers and assessing the trade-off between model complexity and data fidelity, allowing the selection of the most parsimonious representation that best captures the underlying structure of the spectral data. This methodology results is a more refined and interpretable spectral decomposition, aiding in molecular interpretation of data science models in chemical imaging.
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