Artificial Intelligence-Based Hyperspectral Classification of Rare Earth Element-Related Heavy Mineral Sand

Heavy minerals, such as Rutile, Ilmenite and Zircon, and other essential trace elements are important in modern technology development. The integration of hyperspectral imaging and artificial intelligence presents a promising approach for the accurate identification of heavy minerals, especially Rare Earth Element (REE)–bearing phases such as Monazite. This study evaluates three AI classifiers, Support Vector Machine (SVM), Neural Networks (NNs) and Convolutional Neural Networks (CNNs), for their performance in classifying ten different minerals distributed across six grain size groups ranging from 125 μm to over 300 μm. The analysis focuses on how grain size affects spectral reflectance and classification accuracy. Among the tested models, SVM consistently outperformed NN and CNN, achieving the highest precision, recall and spectral similarity, particularly within the 150–300 μm grain size range. CNN showed the lowest performance and frequently misclassified spectrally similar minerals, such as Zircon and Rutile, likely due to its 1D architecture and limited spatial representation. Monazite, notable for its strong Nd3+ absorption features, was accurately identified across applicable grain sizes, highlighting its reliability for REE detection. Spectral Angle Mapper (SAM) analysis confirmed that SVM and NN maintained better spectral similarity than CNN. In general, the results highlight the significant influence of grain size, spectral similarity and dataset size on classification accuracy and the overall effectiveness of AI models in hyperspectral mineral analysis.