Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

Abstract

Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data). Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved.

Research on Hyperspectral Identification of Altered Minerals in Yemaquan West Gold Field, Xinjiang

“…The mineral mapping of the study area is based primarily on the diagnostic absorption band measurement method [17,26]. Based on a programmable feature extraction processing channel, the script for this channel is designed for extracting spectral parameters (absorption depth, wavelength, width, asymmetry).…”

“…Given its advantages of being macroscopic, real-time, periodic, information-rich, and free, hyperspectral remote sensing technology has assumed a leading role in the rapid delineation and evaluation of favourable prospecting areas in a region. This has changed the traditional geological prospecting method, shortened the work cycle, reduced work costs, and improved the efficiency of prospecting [2,[13][14][15][16][17]. In particular, hyperspectral technology combines the traditional two-dimensional imaging technology with spectroscopy technology to obtain both the physiographic information and the spectral information of the constituent features [18].…”

Mapping ultramafic complexes using airborne imaging spectroscopy and spaceborne data in Arctic regions with abundant lichen cover, a case study from the Niaqornarssuit complex in South West Greenland

“…Non-invasive remote sensing is a suitable exploration technique to cover large areas with reduced financial input, if compared to classical approaches. Considerable research has been devoted to the use of multi-and hyperspectral remote sensing technology for geological applications in arid and semi-arid environments (Buckley et al, 2013;Gao et al, 2017;Harris et al, 2005;Huo et al, 2014;Kopačková & Koucká, 2017;Marion & Carrère, 2018;Notesco et al, 2016); however, less research has been devoted to the efficacy of these methods for mineral exploration purposes in the Arctic, and therefore, this is still an open field for research.…”

Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization

“…Several techniques have been proposed in the literature for the analysis of hyperspectral drill-core data and mineral mapping [1,5,6,[12][13][14][15][16]. Among these, band ratios and minimum wavelength maps are useful tools for the evaluation and visualisation of the relative abundance of chemical groups characteristic for specific minerals [17][18][19][20][21]. Another approach for mineral mapping consists of the use of the spectral angle mapper (SAM), a classifier which works based on the similarity measure between an unknown spectrum and reference spectra.…”