Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

Abstract

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.

Information Leakage in Deep Learning-Based Hyperspectral Image Classification: A Survey

“…Hyperspectral images (HSIs) can be viewed as 3D images, where each pixel contains all the band information acquired at that point, providing a richer and more-refined spectral feature for the ground-based object. Therefore, hyperspectral data processing has been widely used in numerous fields, such as precision agriculture [1][2][3], marine resource exploration and mapping [4], water quality analysis [5], military target detection [6], mineral exploration [7], and medical detection and diagnosis [8]. However, the same ground-based object may have different spectral properties due to atmospheric, temperature, spatial resolution, and other effects, and the spectral properties of different ground-based objects may be similar.…”

Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods

“…Laakso et al [32] reported the implementation of an unsupervised machine learning approach based on the integration of self-organizing maps (SOM) and k-means clustering methods implemented in the GisSOM multivariate open-source tool [33,34] to classify the minerals of a single drill core box. In different studies, the RF classifier [2], RF regression [35], and Canonical Correlation Forest (CCF) [36] classifier, which are mainly considered decision tree ensemble methods, have been investigated. A previous study by Acosta et al [2] used mineral liberation analysis (MLA) data for producing training labels for five drill core samples.…”

“…In another study, Barker et al [35] conducted mineral abundance predictions using the RF regression algorithm, which was trained based on labels extracted from resampled micro-X-ray fluorescence (µXRF) imaging. Acosta et al [36] used the CCF classifier for drill core mineral mapping using fused RGB and HSI inputs and resampled MLA data as a source of training examples. These studies demonstrated that the decision tree ensemble methods were effective in retrieving detailed mineralogical information from HSI data.…”