Quantitative evaluation of the impact of band optimization methods on the accuracy of the hyperspectral metal element inversion models

To reduce the high redundancy of band information in hyperspectral data, various band optimization methods have been adopted, which could be divided into two types namely band extraction (e.g., principal component analysis, PCA) and band selection (e.g., Spearman correlation coefficient, SRC). However, the applicability and effectiveness of different band optimization methods were rarely reported in the literature. Therefore, based on the rock sample data of the Baixintan deposit, we compare the performance of two band optimization algorithms (principal component analysis−based band extraction and SRC-based band selection) in inverting metal elements (Cu, Fe, Ni, Cr, Mg) using the adaptive genetic algorithms-gradient boosting regression tree (AGA-GBRT) algorithm. Two band optimization methods have shown different effects in improving the accuracy of target metal elements. The five models with the highest accuracy in metal elements include LT-R-Cu, LT-PCA-Fe, ORI-PCA-Ni, SDT-PCA-Cr, and LT-PCAMg. In the established model, the inversion accuracy of the Cu element is the lowest, possibly due to the high variability of the data itself (coefficient of variation is 3.55). Fe and Ni highly correlated with Cu elements were used to indirectly invert Cu element. Compared with the direct inversion model, the accuracy of the indirect inversion model has increased by 11%. Overall, PCA is more effective than SRC in predicting the content of metal elements in rocks. The conclusion presented in this article provides a ground experimental basis and technical support for future optimization of hyperspectral bands and inversion methods of metal element content in rocks.