Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images

Abstract Mineral classification using hyperspectral imaging represents an essential field of research improving the understanding of geological compositions. This study presents an advancedmethodology that uses an optimized 3D-2D CNNmodel for automatic mineral identification and classification. Our approach includes such crucial steps as using the Diagnostic Absorption Band (DAB) selection technique to selectively extract bands that contain the absorption features of minerals for classification in the Cuprite zone. Focusing on the Cuprite dataset, our study successfully identified the following minerals: alunite, calcite, chalcedony, halloysite, kaolinite,montmorillonite,muscovite, and nontronite. The Cuprite dataset results with an overall accuracy rate of 95.73%underscore the effectiveness of our approach and a significant improvement over the benchmarks established by related studies. Specifically, ASMLP achieved a 94.67%accuracy rate, followed by 3D CNN at 93.86%, SAI-MLP at 91.03%, RNN at 89.09%, SPE-MLP at 85.53%, and SAMat 83.31 %. Beyond the precise identification of specific minerals, ourmethodology proves its versatility for broader applications in hyperspectral image analysis. The optimized 3D-2D CNNmodel excels in terms of mineral identification and sets a new standard for robust feature extraction and classification.