A modified 3D-2D convolutional neural networks for robust mineral identification: Hyperspectral analysis in Djebel Meni (Northwestern Algeria)

This study explores the use of an optimized 3D-2D convolutional neural network (CNN) model for effective mineral identification in the Djebel Meni region of Northwestern Algeria, utilizing hyperspectral imaging data from NASA’s Hyperion EO-1 sensor. Given the challenges posed by remote, complex geological terrains, our approach integrates advanced deep-learning techniques with hyperspectral data to enhance mineral classification accuracy. Following atmospheric correction using the Quac module, spectral signatures of the target minerals—illite, kaolinite, and montmorillonite—from the United States Geological Survey (USGS) spectral library were employed as reference inputs. By leveraging this corrected hyperspectral data, the 3D-2D CNN model was trained to classify these clay minerals with high precision, achieving an overall accuracy of 94.26% and an average class-specific accuracy of 93.93%. These results highlight the model’s robustness in differentiating mineral compositions in geologically challenging contexts, even when limited ground truth data is available. This research underscores the potential of combining hyperspectral remote sensing with sophisticated CNN architectures to advance mineral identification and geospatial analysis, offering valuable insights for mineralogical studies in similar remote regions.