The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

Abstract

Drone-borne hyperspectral imaging is a new and promising technique for fast and precise acquisition, as well as delivery of high-resolution hyperspectral data to a large variety of end-users. Drones can overcome the scale gap between field and air-borne remote sensing, thus providing high-resolution and multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. So far, however, drone-borne imagery has prominently and successfully been almost solely used in precision agriculture and photogrammetry. Drone technology currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. Recently, a few hyperspectral sensors became available for drones, but complex geometric and radiometric effects complicate their use for geology-related studies. Using two examples, we first show that precise corrections are required for any geological mapping. We then present a processing toolbox for frame-based hyperspectral imaging systems adapted for the complex correction of drone-borne hyperspectral imagery. The toolbox performs sensor-and platform-specific geometric distortion corrections. Furthermore, a topographic correction step is implemented to correct for rough terrain surfaces. We recommend the c-factor-algorithm for geological applications. To our knowledge, we demonstrate for the first time the applicability of the corrected dataset for lithological mapping and mineral exploration.

Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

“…2017, 9, 472 3 of 25 processing techniques are more complex and technically expensive. However, a few pioneering works show the great potential of spectral data with high spatial and temporal resolution, for example in monitoring agricultural and natural vegetation [19,37,41,43,46,47]. In particular, Zarco-Tejada et al (2012) [37] used a fixed-wing UAS and imaging spectrometer to collect reflectance signature, narrow-band Photochemical Reflectance Index (PRI) and Sun-Induced Fluorescence (SIF) to assess crop water stress.…”

“…These types of data are important for characterizing natural surfaces, supporting calibration and validation of airborne and satellite images and providing essential information for upscaling measurements from the local to the regional scale [1][2][3][4]. In general, the common way to collect field show the great potential of spectral data with high spatial and temporal resolution, for example in monitoring agricultural and natural vegetation [19,37,41,43,46,47]. In particular, Zarco-Tejada et al (2012) [37] used a fixed-wing UAS and imaging spectrometer to collect reflectance signature, narrow-band Photochemical Reflectance Index (PRI) and Sun-Induced Fluorescence (SIF) to assess crop water stress.…”

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

“…We also describe a detailed methodology for producing 3D hyperclouds, i.e., geometrically correct representations of the hyperspectral datacube, for the display of generated spectral mapping products. The methods presented will be included in the open source Mineral Exploration Python Hyperspectral Toolbox MEPHySTo [7]. We demonstrate the methodology in two areas that differ in geology, climate, and scientific objectives.…”

“…The obtained spectral signatures provide detailed information about the composition of rocks and the occurrence of economic minerals. The hyperspectral instruments are conventionally operated with a nadir viewing angle, comprising different scales of area coverage and spatial resolution by operation on satellite [1,2], airplane [3][4][5][6] or drone [7]. Depending on the acquisition altitude, a varying influence of the atmosphere between sensor and target, as well as illumination differences due to topography, can be observed in the acquired spectral imagery.…”

“…Whereas radiative transfer models rely on the correct input of a set of external parameters and are mainly used for satellite and airborne data, the use of ground targets, dark objects, or flat fields provides a much more straightforward approach. However, these methods require a spatial resolution high enough to resolve spectrally uniform reference target(s) and/or a reasonable knowledge on the spectra of those materials present, and are therefore mainly used for drone-or airborne data with low acquisition altitudes (e.g., [7,15]). …”