A New Approach for Mineral Mapping Using Drill-Core Hyperspectral Image | IEEE Journals & Magazine | IEEE Xplore

A New Approach for Mineral Mapping Using Drill-Core Hyperspectral Image


Abstract:

Hyperspectral remote sensing technology has been successfully applied to geological fields. Drill-core hyperspectral imagery has the characteristics of segmented processi...Show More

Abstract:

Hyperspectral remote sensing technology has been successfully applied to geological fields. Drill-core hyperspectral imagery has the characteristics of segmented processing and large data volume. Due to its simple principle and high accuracy, spectral angle mapping (SAM) has become the most commonly used method for mineral mapping using drill-core hyperspectral images. However, SAM analyzes the entire spectral form of minerals and is not sensitive enough to small differences in drill-core mineral spectra. Compared with traditional machine learning methods, deep learning has more powerful feature learning and feature expression capabilities. In order to improve the mineral mapping accuracy, this letter proposes a new approach called graph convolutional neural networks-SAM (GCNNSAM), which integrates the advantages of deep learning and spectral matching to extract mineral information from drill-core hyperspectral images. Taking the drill-core hyperspectral data near the depth of 240 m as an example, this letter compares the performances of SAM, GCNN, and GCNNSAM mapping methods. The results show that the overall accuracy of the GCNNSAM mapping is 89.23%, and the overall accuracies of SAM and GCNN mapping methods are 80.25% and 83.58%, respectively. Comparing the mineral mapping statistical results of GCNNSAM with the measured geological statistical results, the maximum statistical error of mineral relative content is 1.4%, and the errors are all less than 2%, which verifies the reliability of the proposed method in this study. The method provides a new idea for mineral information acquisition in geological research.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 5511705
Date of Publication: 27 October 2023

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