Semantic segmentation of hyperspectral data for identifying mineral alterations in the Kuhpanj porphyry copper deposit using an improved U-Net architecture

Abstract

Accurate mapping of hydrothermal alteration zones is critical for improving the efficiency of porphyry copper exploration. In the Kuhpanj porphyry copper district (Kerman, Iran), distinguishing phyllic, argillic and propylitic alterations from surrounding lithologies using satellite data remains challenging due to spectral complexity and spatial heterogeneity. This study proposes an improved semantic segmentation framework for spaceborne hyperspectral imagery, exploiting 41 bands of PRISMA data to delineate alteration zones at the deposit scale. A modified U-net architecture was developed that employs a dual-path design for the concurrent extraction of spectral and spatial features: one branch processes pixel-wise spectra across the 41 bands, while the second branch captures local spatial context within 256 × 256 × 41 patches. The performance of the proposed network was benchmarked against a V-net architecture using a confusion-matrix-based evaluation. The proposed model achieved an F1-score of 86% while being trained on a limited labelled dataset, and it requires substantially fewer trainable parameters than the reference architecture, highlighting its efficiency for data-constrained exploration scenarios. The results demonstrate that the new dual-path U-net significantly enhances the reliability of alteration mapping from PRISMA hyperspectral data and provides a computationally efficient deep-learning solution for processing high-dimensional geoscientific imagery. This contribution extends current applications of convolutional neural networks in mineral exploration by introducing a tailored architecture that improves both accuracy and model compactness for hyperspectral semantic segmentation.