Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops

Abstract

Abstract:Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called "hyperclouds", i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements.

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

“…Terrestrial imaging spectrometry for mapping of near vertical geological outcrops has been the topic of many studies in recent years [35][36][37][38]. This approach has been applied in the Arctic by scanning the vertical cliffs from an opposing location such as a neighboring mountain [48,60]. However, such approach is not always feasible where data are to be acquired on a routine operational basis to provide information on mineralogy as part of large-scale mapping operations.…”

“…The aim is, therefore, not to provide the most accurate dataset but to allow the best data acquisition without penalizing the other exploration activities in a time and cost efficient manner. Detailed atmospheric/radiometric and topographic corrections are beyond the scope of this paper and will be discussed in a forthcoming paper [60].…”

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

“…However, the set-up of commercially available sensors is relatively inflexible. Past research has shown that reflectance in the short and medium wave infrared bands is more useful for sediment classification, and that hyperspectral sensors can accurately map geological properties of rock, e.g., [51]. Recent studies have mounted hyperspectral cameras to drones for a range of purposes [52,53].…”

Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

“…Aside vegetation, the VNIR spectral range can bring valuable information on other objects of the interest such as Fe 3+ -bearing minerals [20], alteration minerals [24], rare earths [6], organic component [25] and optically active water substances [26]. Because of a relatively unstable platform and extremely challenging acquisition geometries with respect to sun illumination, very minute corrections are required [27,28].We propose an integrated remote sensing approach utilizing all the different levels of data resolutions to address these challenges; from satellite-to aircraft-to RPAS-based. The multiscale and multisource data combination entails the full scope of available remote sensing imaging solutions from space-borne to RPAS-based sensors as well as a dedicated field campaign for training and validation of the algorithms.…”

“…Aside vegetation, the VNIR spectral range can bring valuable information on other objects of the interest such as Fe 3+ -bearing minerals [20], alteration minerals [24], rare earths [6], organic component [25] and optically active water substances [26]. Because of a relatively unstable platform and extremely challenging acquisition geometries with respect to sun illumination, very minute corrections are required [27,28].…”