Evaluation of support vector machine classifiers for lithological mapping using PRISMA hyperspectral remote sensing data: Sahand–Bazman magmatic arc, central Iran

Abstract

Mineral exploration is highly dependent on an accurate lithological map of a study area, which provides comprehensive information on geologic features for exploration target zones. Nowadays, the processing of hyperspectral image data for lithological mapping and mineral exploration using machine learning (ML) algorithms has greatly developed. The recently launched Italian hyperspectral sensor ‘PRecursore IperSpettrale della Missione Applicativa (PRISMA)’ offers an excellent capability for mineral detection and object classification with superior accuracy and efficiency for lithological mapping and mineral exploration. In this study, the performance of the support vector machine (SVM) algorithm was evaluated for processing PRISMA datasets to generate lithological maps of the Sar Cheshmeh porphyritic copper ore deposit in the Sahand–Bazman magmatic arc in central Iran. Three different SVM kernels, namely linear (LSVM), quadratic (QSVM) and cubic (CSVM), were comparatively evaluated for data classification in lithological mapping. The SVM classifiers were trained on the basis of prior knowledge from previous studies and field surveys. Approximately 5000 pixels from 14 different classes were used for training. There was a large misclassification between granodiorites and altered granodiorites in the LSVM result (78.3% accuracy for altered granodiorites), but this was greatly reduced in the QSVM and CSVM methods (with 96.1% and 99.1% accuracy, respectively). A significant improvement in classification was also seen for the vegetation, mine pits and Razak volcanism classes (with varying accuracy values). It is noteworthy that nine of the 14 classes had less than 400 training pixels and only one class had more than 1000 pixels used for training, indicating the power of ML for such studies. LSVM was the best method for mapping dacites with maximum accuracy (100%), but this accuracy was slightly lower for QSVM and CSVM (both had 97.9% accuracy). The results show that the LSVM, QSVM and CSVM methods achieved an accuracy of 80.22%, 85.81% and 86.05%, respectively, in the final classification. This study advocates the optimal SVM classifier (CSVM classifier) using PRISMA hyperspectral images for accurate lithological mapping for mineral exploration in metallogenic provinces.