Phase-Locked SNR Band Selection for Weak Mineral Signal Detection in Hyperspectral Imagery

TL;DR

This paper introduces a two-stage spectral band selection and unmixing framework for hyperspectral mineral detection, improving accuracy by removing noisy bands and preserving spectral features, demonstrated in the Cuprite mining district.

Contribution

It presents a novel integrated approach combining SNR-based band selection, spectral smoothing, and unmixing techniques for enhanced mineral detection in hyperspectral imagery.

Findings

Improved unmixing accuracy with the proposed pipeline.

Enhanced detection of weak mineral zones.

Effective spectral dimensionality reduction in geological HSI.

Abstract

Hyperspectral imaging offers detailed spectral information for mineral mapping; however, weak mineral signatures are often masked by noisy and redundant bands, limiting detection performance. To address this, we propose a two-stage integrated framework for enhanced mineral detection in the Cuprite mining district. In the first stage, we compute the signal-to-noise ratio (SNR) for each spectral band and apply a phase-locked thresholding technique to discard low-SNR bands, effectively removing redundancy and suppressing background noise. Savitzky-Golay filtering is then employed for spectral smoothing, serving a dual role first to stabilize trends during band selection, and second to preserve fine-grained spectral features during preprocessing. In the second stage, the refined HSI data is reintroduced into the model, where KMeans clustering is used to extract 12 endmember spectra (W1 custom), followed by non negative least squares (NNLS) for abundance unmixing. The resulting endmembers are quantitatively compared with laboratory spectra (W1 raw) using cosine similarity and RMSE metrics. Experimental results confirm that our proposed pipeline improves unmixing accuracy and enhances the detection of weak mineral zones. This two-pass strategy demonstrates a practical and reproducible solution for spectral dimensionality reduction and unmixing in geological HSI applications.