We evaluated the opportunities and performance of a new type of systematic pixel sharp calibration site for airborne hyperspectral mineral mapping in the environment of an operational copper deposit in Mongolia. The calibration site was designed to be used for estimation of sensitivity and quantification of key minerals in individual pixels in specific geological scenarios. The layout of the calibration site was done with two different copper-containing rock samples, a low copper-containing rock material from the mine, tailing material from the mine, and calibration materials with well-defined known spectral features. The scaled coverage of the sample materials was designed to develop statistical approaches to quantify target minerals in airborne surveys on a pixel-based approach. The data collection included the description of the calibration materials with geochemical, x-ray diffraction, and microscopic and electron raster microscopic methods. Using visible and near-infrared airborne sensors and shortwave infrared airborne sensors, data of the calibration site were collected with multiple repeats from six altitudes. After rectification and atmospheric correction of pixels, sharp measurements of absorption features of clay minerals at 1400, 1900, and 2200 nm were performed and statistically analyzed. Correlations between coverage and absorption features especially around 2200 nm are shown, and influences of flight altitude on sensitivity of the detection and the stability of the measurements are investigated. The results of the calibration field are used for the quantitative estimations of clay minerals in an exploration area near the mine site. The results are also shining new light on methodologies for ground truthing in hyperspectral surveys. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.