Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

Abstract

While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors, their application is mostly confined to nadir imaging orientations. Oblique hyperspectral imaging has been impeded by the absence of robust registration and correction protocols, which are essential to extract accurate information. These corrections are especially important for detecting the typically small spectral features produced by minerals, and for infrared data acquired using pushbroom sensors. The complex movements of unstable platforms (such as UAVs) require rigorous geometric and radiometric corrections, especially in the rugged terrain often encountered for geological applications. In this contribution we propose a novel correction methodology, and associated toolbox, dedicated to the accurate production of hyperspectral data acquired by UAVs, without any restriction concerning view angles or target geometry. We make these codes freely available to the community, and thus hope to trigger an increasing usage of hyperspectral data in Earth sciences, and demonstrate them with the production of, to our knowledge, the first fully corrected oblique SWIR drone-survey. This covers a vertical cliff in the Dolomites (Italy), and allowed us to distinguish distinct calcitic and dolomitic carbonate units, map the qualitative abundance of clay/mica minerals, and thus characterise seismic scale facies architecture.

Full text

Remote Sens. 2022, 14, 5. https://doi.org/10.3390/rs14010005 www.mdpi.com/journal/remotesensing
Article
Mineralogical Mapping with Accurately Corrected Shortwave
Infrared Hyperspectral Data Acquired Obliquely from UAVs
Samuel T. Thiele
1,
*, Zakaria Bnoulkacem
1,2
, Sandra Lorenz
1
, Aurélien Bordenave
3
, Niccolò Menegoni
4
,
Yuleika Madriz
1
, Emmanuel Dujoncquoy
5
, Richard Gloaguen
1
and Jeroen Kenter
5
1
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology,
09599 Freiberg, Germany; z.bnoulkacem@hzdr.de (Z.B.); s.lorenz@hzdr.de (S.L.);
y.madriz-diaz@hzdr.de (Y.M.); r.gloaguen@hzdr.de (R.G.)
2
Department of Photogrammetry and Cartography ESGIT, IAV Hassan II University, Rabat 10101, Morocco
3
Applied Geology and Sedimentology, Technopole Hélioparc, 64000 Pau, France;
aurelien.bordenave@ageos-science.com
4
Department of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy;
niccolo.menegoni01@universitadipavia.it
5
TotalEnergies, 64000 Pau, France; emmanuel.dujoncquoy@total.com (E.D.);
jeroen.kenter@totalenergies.com (J.K.)
* Correspondence: s.thiele@hzdr.de
Abstract: While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors,
their application is mostly confined to nadir imaging orientations. Oblique hyperspectral imaging
has been impeded by the absence of robust registration and correction protocols, which are essential
to extract accurate information. These corrections are especially important for detecting the typically
small spectral features produced by minerals, and for infrared data acquired using pushbroom sen-
sors. The complex movements of unstable platforms (such as UAVs) require rigorous geometric and
radiometric corrections, especially in the rugged terrain often encountered for geological applica-
tions. In this contribution we propose a novel correction methodology, and associated toolbox, ded-
icated to the accurate production of hyperspectral data acquired by UAVs, without any restriction
concerning view angles or target geometry. We make these codes freely available to the community,
and thus hope to trigger an increasing usage of hyperspectral data in Earth sciences, and demon-
strate them with the production of, to our knowledge, the first fully corrected oblique SWIR drone-
survey. This covers a vertical cliff in the Dolomites (Italy), and allowed us to distinguish distinct
calcitic and dolomitic carbonate units, map the qualitative abundance of clay/mica minerals, and
thus characterise seismic scale facies architecture.
Keywords: infrared; hyperspectral; uncrewed aerial vehicle; calcite; dolomite
1. Introduction
Uncrewed aerial vehicles (UAVs) have revolutionised geological mapping by facili-
tating detailed and quantitative outcrop characterisation. Recent reviews highlight a va-
riety of applications, including geotechnical investigations, geohazard assessment, struc-
tural analysis, stratigraphic mapping and teaching [1–4]. Most of these studies relied on
an interpretation of outcrop colour captured using conventional lightweight RGB cam-
eras, and geometric information from light detection and ranging (LiDAR) or structure
from motion multi-view stereo (SfM-MVS) workflows [5].
Recently, miniaturised hyperspectral cameras covering the shortwave infrared range
(SWIR; 1000–2500 nm) provided an opportunity for similarly detailed outcrop studies,
but with an added hyperspectral dimension that allows the distribution and abundance
of many rock-forming minerals (e.g., carbonates, clays, micas, chlorites, amphiboles and
other minerals with diagnostic features in the SWIR; [6]) to be mapped.
Citation: Thiele, S.T.; Bnoulkacem,
Z.; Lorenz, S.; Bordenave, A.;
Menegoni, N.; Madriz, Y.;
Dujoncquoy, E.; Gloaguen, R.;
Kenter, J. Mineralogical Mapping
with Accurately Corrected
Shortwave Infrared Hyperspectral
Data Acquired Obliquely from
UAVs. Remote Sens. 2022, 14, 5.
https://doi.org/10.3390/rs14010005
Academic Editors: Prasad S.
Thenkabail and Norman Kerle
Received: 20 October 2021
Accepted: 17 December 2021
Published: 21 December 2021
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
tional affiliations.
Copyright: © 2021 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://cre-
ativecommons.org/licenses/by/4.0/).

Remote Sens. 2022, 14, 5 2 of 22
Previous studies using tripod-mounted sensors have demonstrated the significant
potential of SWIR data for geological mapping applications (e.g., [7–10]), but have been
limited in resolution by access constraints and oblique viewing angles. These constraints
can be especially challenging when scanning cliffs or mines, as (1) upward looking view-
ing angles result in highly oblique images and associated distortions, and (2) rockfall haz-
ards and limited access to scree slopes prevents deployment of (generally heavy) equip-
ment for close-up mapping.
To avoid these limitations, hyperspectral sensors are increasingly being deployed on
UAV platforms. A plethora of UAV compatible visible-near infrared (VNIR) sensors are
available (cf. [11]); however, weight limitations present significant challenges for SWIR
cameras, which require heavy and energy intensive heat pumps to cool the detector to
<−100 °C before spectrally accurate data can be acquired. For this reason, SWIR sensors
have only recently become small and lightweight enough for deployment on UAVs [12–
14].
Most hyperspectral sensors do not acquire an entire data cube simultaneously, but
either acquire bands sequentially (e.g., the Senop Oy Rikola) or by splitting a narrow slit
of incoming light across a 2D sensor to acquire 1-pixel wide lines of continuous spectral
data (pushbroom sensors; [11]). Currently available UAV-compatible SWIR cameras all
use pushbroom acquisition modes. As a result, sensor movement during data acquisition
causes complex geometric distortions that must be corrected before usable data can be
obtained. Gimbal systems can be employed to minimise the influence of vibrations, but
distortions due to the movement of the UAV along its flight path are unavoidable (espe-
cially for pushbroom sensors, which require this movement to cover the area of interest;
Figure 1). These distortions are larger for UAV platforms than for conventional aircraft,
due to their lighter weight and the higher resolution ground sampling associated with
low flight altitudes (see [15] for a detailed description of the challenges associated with
pushbroom sensors).
For frame-sensors, image matching and computer vision techniques can be used to
co-register hyperspectral bands and so correct for geometric distortions [10,16,17].
Pushbroom sensors are more challenging; an inertial processing unit (IMU) and global
positioning system (GPS) is needed to accurately quantify the movement of the sensor
and remove distortions by back-projection (e.g., [15]). Despite these challenges, several
authors have successfully used pushbroom VNIR sensors to collect extremely high-reso-
lution (2–10 cm) data with reasonable levels of spatial accuracy (5–100 cm; [15,18–20]). All
of these authors corrected distortions resulting from UAV movement using closed-source
geometric corrections implemented in the PARGE software [21]. This approach applies a
ray-tracing algorithm to locate the centre point of each hyperspectral pixel on a high-res-
olution digital elevation model (DEM) and then interpolates between these to fill gaps as
necessary. Due to uncertainties in measuring sensor position and orientation using an in-
ertial processing unit (IMU), most authors then warp the resulting hyperspectral ortho-
mosaic to fit measured ground control points [21] or a high-resolution photogrammetric
RGB orthomosaic [18,20].
Due to their reliance on raster datasets, this correction method performs poorly in
topographically complex areas, which cannot be compressed into a 2.5D raster [15]. Areas
of geological interest and good exposure, such as cliffs, quarries and mines, often include
sub-vertical faces with multiple orientations, so cannot be projected onto a 2D grid
(DEM/orthomosaic) without introducing projective distortions that significantly degrade
data quality [10,22,23]. To mitigate these issues and facilitate the collection of geological
data from potentially important but otherwise inaccessible outcrops, a rapidly increasing
number of methods are being developed to fuse remotely sensed hyperspectral data with
dense 3D point clouds to create hyperclouds [8,10,17,23,24] that objectively record outcrop
geometry and composition.

Remote Sens. 2022, 14, 5 3 of 22
Figure 1. Data acquisition procedure using a UAV-mounted pushbroom sensor. The sensor is
mounted on a UAV, which moves over the target of interest (a) and captures light from within a
thin scanline (green) to progressively construct a hyperspectral data cube (b). This data cube must
then be geometrically corrected to a spatially accurate hyperspectral swath (c) that can be analysed
or interpreted to map, e.g., mineralogy.
In this contribution, we present (1) a novel and open-source python workflow for
applying robust geometric and radiometric corrections to hyperspectral data, regardless
of acquisition and target geometry, and (2) results from a demonstration survey con-
ducted to map the distribution of dolomitic, calcitic and argillaceous carbonate lithologies
exposed along cliffs near Passo Giau, Italy (Figure 2). This transect is large enough (>500
× 100 m) to be directly analogous to geological structures resolvable in subsurface seismic
data.
2. Location
The study area is located on the south-eastern side of the Gusela del Nuvolau outcrop
[25], north of Passo Giau in the central-eastern Italian Dolomites (Figure 2). This 600 m
long and 300 m high cliff (Figure 3) exposes remnants of an exhumed Cassian carbonate
platform of the Upper Ladinian to Lower Carnian age, including the transition from plat-
form (Cassian Dolomite Formation) to basinal (San Cassiano Formation) facies [26]. The
Cassian Dolomite is a greyish-white fine grained dolostone with sucrose texture that, due
to the pervasive dolomitisation of the Cassian platform, preserves few original character-
istics of the rock (e.g., original textures, fossils or sedimentary structures; [27]). The San
Cassiano Formation comprises alternating marls and limestones with oolitic/bioclastic
grainstone [26,27]. In general, the geometries of the Cassian platform and the San Cassiano
Formation (Figure 3) suggest a north-east (NE) progradation of the slope deposits onto
the basinal unit [25,28].

Remote Sens. 2022, 14, 5 4 of 22
Figure 2. Location map (a) of the study site and simplified geological map of the surrounding area
(b; cf. [27]). The Gusela del Nuvolau outcrop is indicated by the yellow dashed oval. This exposes
Middle/Upper Triassic stratigraphy (c) of the central-eastern Italian Dolomites [26]: DP, Dolomia
Principale; TVZ, Travenanzes Formation (Fm.); HKS, Heiligkreutz Fm.; DCS, Cassian Dolomite Fm.;
SCS, San Cassiano Fm.; WEN, Wengen Fm.; SCI, Sciliar Fm.; IMF, Mt. Fernazza Fm.
Figure 3. Photograph of the study area (Gusela del Nuvolau cliff). The carbonate body contains
dolomitic and calcitic units, but these cannot be distinguished using the naked eye. Cliff is 250–300
m high and 600 m long. The grey rectangle shows the approximate extent of the hyperspectral sur-
vey.

Remote Sens. 2022, 14, 5 5 of 22
The outcrop is accessible today due to the Alpine orogenic event, during which the
Dolomites underwent only mild tectonic deformation [29–31]. This allowed preservation
of seismic scale depositional geometries of the Middle Triassic platform [25] at Gusela del
Nuvolau (Figure 3).
Geological determination of lithofacies partitioning (i.e., quantification of spatial jux-
taposition and proportions of geological rock types) are typically obscured by a combina-
tion of access limitations and subjective, non-reproducible interpretation by geologists.
The primary objectives to deploy the UAV and VNIR-SWIR camera are to provide an ac-
curate and objective method for mapping lithological and mineralogical variations (here
calcite, dolomite and clay), which benefits the reconstruction of realistic and accurate
models in the subsurface for the energy industry (geothermal, CO2 sequestration and hy-
drocarbons). The Passo Giau outcrop was selected as a technical test site to demonstrate
the method’s potential, test correction procedures and validate the remotely sensed hy-
perspectral imagery.
3. Methods
3.1. Hyperspectral Data Acquisition
We used a HySpex Mjolnir VS-620 system (Table 1), which allows the concurrent
acquisition of hyperspectral data in the VNIR range (400–1000 nm, HySpex Mjolnir V-
1240 sensor with 1024 spatial pixels) and the SWIR range (970–2500 nm, HySpex Mjolnir
S-620 sensor with 620 spatial pixels). These sensors operate as pushbroom scanners to cre-
ate a spectral data cube by constant acquisition and movement along the target of interest
(Figure 1).
Unlike typical nadir-oriented sensor setups, we mounted the Mjolnir sideways in a
Gremsy gStabi H16 XL gimbal so that the camera faced the cliff face and the scan line was
sub-vertical. This setup was carried by a custom-built octocopter UAV (Figure 4) which,
with the equipped payload of ~8 kg, can achieve flight times of 15–20 min under ideal
conditions.
In the current study, two swaths of hyperspectral data (hereafter referred to as Swath
A and Swath B) were acquired by flying gently descending flight lines (parallel to bed-
ding) at 140 (Swath A) and 90 m (Swath B) distance from the cliff (Figure 5). Due to gen-
erally sunny conditions, a frame rate of 50 fps and resulting integration times of 10 (VNIR)
and 20 ms (SWIR) was sufficient to ensure adequate image exposure, and a velocity of ~2
m/sec chosen to give approximately square pixels. This configuration resulted in a theo-
retical ground sampling distance of 8 cm (Flight A) and 5 cm (Flight B) for the SWIR sensor
(Table 1).
High winds (~14 m/s) and poor GPS signal presented significant challenges during
data acquisition, as pushbroom sensors require flight lines to be conducted in as smooth
a fashion as possible. These were (partially) mitigated by orienting the scanner such that
it faced the cliff while the UAV was flying forwards, as opposed to orienting the scanner
in a forward-facing direction and flying the UAV sideways. This configuration was found
to be more stable, because the UAV does not need to constantly manoeuvre to maintain a
fixed heading. The gently descending flight lines also helped to stabilise the UAV.

Remote Sens. 2022, 14, 5 6 of 22
Table 1. Sensor parameters of the HySpex Mjolnir VS-620 combined hyperspectral imaging and
photogrammetry system deployed in the study. Data from the V-1240 were spatially downsampled
(binned) to match the S-620 sensor for this study.
HySpex V-1240 HySpex S-620 Sony Alpha 6400
Spectral range 400–1000 nm 970–2500 nm RGB true color
Spatial pixels 1240 620 6000 × 4000
Spectral channels/sampling 200/3 nm 300/5.1 nm 3/-
FOV 20 deg (0.27 mrad/px) 20 deg (0.54 mrad/px) 40.6 × 60.9 deg (0.18 mrad/px)
Weigh
t
7.253 kg (without cables)
Figure 4. Image of the UAV and Hyspex Mjolnir-VS mounted sideways in the Gremsy gStabi H16
XL gimbal. Lens caps for the three sensors (VS-1240, S-620 and Sony Alpha) can be seen on the right
side of the camera.

Remote Sens. 2022, 14, 5 7 of 22
Figure 5. Post processed flight paths for flight A and B draped over the photogrammetric model.
Coloured points show the camera position at each frame (line of hyperspectral data), with blue at
the start of each survey and red at the end. The closely spaced red vectors show the camera viewing
direction at each of these positions. The transparent grey area shows the map-view extent of the
surveyed section of cliff. The location of the Zenith calibration panels (Section 3.5) and ground-truth
spectra (Section 3.7) are also overlain for reference.
3.2. Photogrammetry
The HySpex Mjolnir camera was also fitted with a co-aligned 24.2 megapixel mirror-
less DSLR camera (Sony Alpha 6400) that captured an RGB image every 200 hyperspectral
frames, resulting in 205 images with a horizontal overlap of ~80%. These images were
supplemented by an additional 144 images captured using a DJI Spark and 132 images
from a Parrot Anafi to ensure the entire outcrop was adequately covered, and then pro-
cessed using Agisoft Metashape (v. 1.6.3 build 10732) to generate a sparse cloud contain-
ing 249,329 points using structure from motion (SfM). This sparse cloud was manually
filtered to remove outliers and then georeferenced using 14 ground control points sur-
veyed using a PPK GNSS rover (8 points; 3–5 cm accuracy) and Total Station (6 points;
~10 cm accuracy) before re-running the bundle adjustment to mitigate potential photo-
grammetric distortions. Finally, a dense cloud containing 99,141,154 points was created
using the multi-view stereo technique implemented in Agisoft Metashape and exported
to CloudCompare [32], where it was subsampled by removing closely spaced points until

Remote Sens. 2022, 14, 5 8 of 22
they had an average spacing of 5 cm, and clipped to the region of interest. Details of the
photogrammetric reconstruction can be found in Supplementary Materials S1.
3.3. Geometric Corrections
As with all airborne pushbroom scanners, complex geometric distortions caused by
changes in position and orientation of the camera during flight require correction before
a usable image can be obtained. These corrections rely on precise position and orientation
data recorded by an Applanix APX-20 inertial processing unit (IMU) mounted within the
Hyspex Mjolnir camera to model and remove geometric distortions. However, established
correction workflows (e.g., [11,15]) rely on 2.5D representations of scene geometry and so
cannot correct highly oblique surveys covering e.g., cliffs or open-pit mines. For this rea-
son we have developed and implemented a novel geometric correction procedure within
the open-source hylite toolbox (cf. [10]).
First, as was previously described by [15], we processed the APX-20 data using Trim-
ble’s PosPAC software to apply post processed kinematic (PPK) corrections to the GNSS
position data and fuse this with accelerometer and orientation measurements from the
IMU using the Applanix Inertial Navigation System (INS) fusion toolbox. Data from the
onboard GNSS was corrected using a Trimble base station that we set up on site using a
Zephyr 2 GNSS Antenna and R5 receiver. The position of our base station was determined
using static measurement with an occupation time of 5 h and corrected using Trimble
Business Centre and permanent base station data from Bolzano, Bozen, Cercivento, Rov-
ereto, Padova and Venezia.
The cliffs obscured a significant part of the sky during data acquisition, meaning the
onboard GNSS receiver only recorded data from 5–12 satellites during the flight, causing
highly degraded GNSS solutions (PDOP < 4 for ~30% of the flight time). Because of this,
we processed each flight line separately and manually removed the degraded sections.
This lowered the overall quality of the processing, especially because it meant only one
IMU calibration figure was available for each survey, creating discrepancies between the
two flight lines. Each flight was processed twice using the INS fusion algorithm in PO-
SPac; the first solution was used to identify the lever-arm between the GNSS receiver and
the IMU, and the second to obtain the final smoothed best estimate (SBET) trajectory.
This trajectory was exported and processed using software provided by Hyspex
(Hyspex Nav) to derive camera orientation and position measurements for the pixel-cen-
tres of each hyperspectral line (Figure 5). A boresight correction was also applied using
HySpex Nav and values determined from ground control points surveyed during a cali-
bration flight conducted by the sensor manufacturer.
We then used hylite [10] to project the photogrammetric point cloud onto the hyper-
spectral image using the measured position and orientation of the sensor during the ac-
quisition of each line of hyperspectral pixels, and so built a sparse mapping matrix (M)
that relates points in the scene to pixels in each swath. M has dimensions such that each
row corresponds to a point in the point cloud and each column the (flattened) index of a
hyperspectral pixel. Mij thus represents the visibility of point i (with position ui) in pixel j
given the camera position vector c and orientation matrix R at the start (n) and end (n + 1)
of the hyperspectral frame. Note that unlike conventional frame sensors, pushbroom ac-
quisition modes often image the same point multiple times (due to rotation of the sensor
during acquisition), so M represents a many to many mapping between points and pixels.
For pushbroom sensors, a point can be considered to be visible from pixel j if it
crossed the sensor scan line during the exposure of this pixel, resulting in a change in sign
(sgn) of the dot product between the camera motion vector and relative point position
vector (in camera coordinates), satisfying:
𝑠𝑔𝑛((𝒖−𝒄)⋅𝑹)≠𝑠𝑔𝑛((𝒖−𝒄)⋅𝑹) (1)
This will be true only if the pixel crossed the sensor plane between frames n and n +
1. Points outside the sensor field of view are then culled (based on a perspective projection

Remote Sens. 2022, 14, 5 9 of 22
and focal length f), so that points are only considered visible if they satisfy both Equation
(1) and:
−1<
𝑓
× (𝒖−𝒄)⋅𝑹
(𝒖−𝒄)⋅𝑹<1 (2)
where either m = n or m = n + 1 (cf. [33] for an overview of the mathematics behind per-
spective projection).
Constructing M for large clouds (10–100 million points) and hyperspectral swaths
(>1000 s of lines) would require prohibitively expensive computations, as Equations (1)
and (2) need to be evaluated for every point and every line of pixels. Our algorithm re-
duces the number of required computations by dividing each hyperspectral image into
200 pixel wide chunks and, for each chunk, considering only points that cross the sensor
plane between its first and last line, and are within the cross-track field of view. This form
of frustum culling (a commonly applied technique for rapid 3-D rendering; [33]) signifi-
cantly reduces the computations required by projecting only a small subset of the total
point cloud onto each line of pixels, by assuming the camera position and orientation vary
smoothly over time (a requirement for collecting usable data).
To facilitate the identification and removal of occluded points (that map onto the
sensor after a perspective projection but were not visible due to a light-blocking object in
the foreground), the inverse of the distance between each point-pixel pair were stored as
the elements of the mapping matrix M. This allows efficient querying of point-pixel depths
(by computing the argmax of M along its columns) and the construction of a depth buffer
[33] that can be used to identify and remove elements in M that result from occluded
points.
Different normalisations or filtering can then be applied to M to derive a normalised
mapping matrix, denoted Mf, which can be multiplied by a vector of pixel spectra (Ps) or
point attributes (Va) to derive back-projected per-point point spectra (Vs) or a projected
image of point attributes (Pa): 𝑴⋅𝑷=𝑽 (3)
𝑴
⋅𝑽=𝑷 (4)
The normalisation method will determine how many to many relationships between
points and pixels will be handled. If Mf is calculated by normalising M such that its col-
umns sum to one, then the spectra in all of the pixels that a point is visible from will be
averaged (weighted by their proximity to the sensor). Alternatively, the number of points
contained in each pixel (which is proportional to the on-ground pixel size, or footprint)
can be calculated by summing the columns of M and used to derive a filtered Mf that maps
the spectra from the closest (smallest footprint) pixel directly to each point (with no aver-
aging), so that maximum spatial resolution is retained.
Code for constructing M and performing these filtering operations to rapidly transfer
data between a SfM-MVS point clouds and pushbroom hyperspectral imagery has been
implemented in python as part of the open-source hylite package and is available at
https://github.com/samthiele/hylite (accessed on 18 December 2021).
3.4. Boresight Optimisation
Despite the accuracy of the Applanix IMU under optimal conditions, a combination
of GPS error, IMU orientation uncertainty, and the boresight between the IMU and each
hyperspectral sensor, resulted in a relatively large offset (1–2 m/10–20 pixels) between
projected hyperspectral pixels and their actual location on the point cloud (identified us-
ing RGB colours from the SfM-MVS). These errors were not ameliorated by applying a
standard boresight calibration using values estimated using SfM during the earlier cali-
bration flight (cf. [15]), probably because they result from rotations of the local coordinate
system induced by poor GPS signal during the survey and IMU calibration figure (cf.

Remote Sens. 2022, 14, 5 10 of 22
Section 3.3), rather than the physical sensor boresight. This unacceptably large error was
reduced by developing a novel boresight optimisation algorithm that finds the boresight
values which result in the highest correlation between projected RGB colours and equiv-
alent colours in the SfM-MVS point cloud.
First, we assume that the combined IMU alignment errors can be approximated by a
single rigid body rotation between the IMU and the SfM-MVS point cloud coordinate sys-
tems. Position errors most likely also result in a lever-arm offset, but this constant offset
will have less effect on projected pixel location than orientation error (which increases
proportionally to the sensor–target distance).
An optimal boresight adjustment was then found to correct this rotation (IMU misa-
lignment) for each flight line, by repeatedly projecting the red, green and blue bands of
each of the VNIR images onto a sub-sampled point cloud and maximising the correlation
with point colours derived from the SfM-MVS model. This optimisation was achieved
using the iterative least-squares algorithm implemented in SciPy (scipy.optimise.mini-
mize; [34]).
Following this procedure, we were able to co-register the two hyperspectral swaths
to within ~1 pixel of each other, and ~2 pixels of the SfM-MVS point cloud (based on a
visual assessment of the projected point colours; Figure 6). A quantitative assessment of
the reprojection error was not possible, as surveyed ground control points could not be
placed on the cliffs. The projection-mapping matrix calculated using the boresight values
was then saved for each flight for subsequent use during radiometric correction (Section
3.5) and fusion of the swaths (Section 3.6).
Figure 6. Geometrically corrected true-colour hyperspectral datasets before (a) and after (b)
boresight optimisation for each flight. Outlines of the 1 m
2
grey and black calibration panels as
mapped on the SfM-MVS point cloud (c) are overlain for reference. While there is significant room
for improvement, coregistration error dropped from ~2.5 m before optimisation to ~0.2 m after-
wards. Interestingly the optimal boresight values (roll, pitch, yaw in degrees) are substantially dif-
ferent between the two flights (d), even though these were conducted within an hour of each other.
This suggests that they represent ‘virtual’ boresight values that correct for a misalignment between
the global coordinate system and the IMU coordinate system caused by poor GPS signal during
IMU calibration, rather than the real sensor boresight.

Remote Sens. 2022, 14, 5 11 of 22
3.5. Radiometric Corrections
The hyperspectral data for each flight were preprocessed using dedicated software
provided by HySpex (HyspexRad) to convert raw digital numbers into radiance and cor-
rect for smile and keystone distortions. Calibration spectra from two Zenith calibration
panels (R = 5% and 50%) placed at the base of the cliff in each scene (cf. Figure 5) were
then extracted by back-projection onto a subset point cloud containing only the panels.
Additionally, indirect illumination was quantified by averaging pixels of a shaded Spec-
tralon R90 panel (R = 90%) that was placed in front of the camera following each survey
(after the UAV had landed).
Following the method of [35], these spectra were used to characterise the down-
welling sunlight, skylight and path radiance in each scene. The measured radiance (r) of
each panel with known reflectance spectra R can be described by the approximately Lam-
bertian reflection of a mixture of diffuse skylight and sunlight [35]:
𝑟 = 𝑅×𝑎⋅𝑆+cos𝜃⋅𝐼+𝑃 (5)
where I and S are the incident sunlight (direct illumination) and skylight (indirect illumi-
nation) spectra, respectively; a is the fraction of visible sky from the panel, θ is the angle
between the panel normal and downwelling sunlight and P is the path radiance. We find
the three unknowns in this equation (S, I and P) by solving the following linear system:
𝑎𝑅cos (𝜃)𝑅1
𝑎𝑅cos (𝜃)𝑅1
𝑎𝑅00
𝑆𝐼
𝑃=(𝑟𝑟𝑟) (6)
where A, B and C correspond to each of the three panels used (5%, 55% and the (shaded)
90% panel, respectively). The panels’ sky view factor (a) and incidence angle (θ) were cal-
culated using the SfM-MVS model, as described in the following paragraph. Note that the
inclusion of a fully shaded panel (such that for this panel I is known to be 0) ensures
proper characterisation of approximately omnidirectional skylight (cf. [35]).
These three illumination spectra (Figure 7c,d) allowed us to model and correct for the
influence of atmospheric and topographic effects. As described in detail by [35], incident
and viewing angles for each pixel were calculated by projecting the 3-D point cloud onto
each hyperspectral image (cf. Equation (4)) and computing an Oren-Nayar [36] bidirec-
tional reflectance distribution function (BRDF) that simulates the fraction α of sunlight
reflected towards the camera from each pixel. Sky view factors (a) were calculated for each
point using the PCV plugin [37] in CloudCompare, and projected from the point cloud
onto each pixel in the hyperspectral image. This allowed per-pixel reflectance (R) to be
estimated from measured radiance (r) using the following equation [35]:
𝑅= 𝑟−𝑃
𝛼⋅𝐼+𝑎⋅𝑆 (7)
where the numerator represents the at-target reflected radiance, and the denominator the
combined direct and indirect illumination across the scene.

Remote Sens. 2022, 14, 5 12 of 22
Figure 7. Measured calibration panel spectra (a,b) for each flight and derived illumination source
spectra (c,d). These spectra were used to model and correct for illumination effects (atmospheric
and topographic) separately in each flight. Note that the path radiance is essentially negligible, as
expected due to the short sensor–target distance, but that skylight accounts for 25% to 50% of the
total illumination during both flights.
3.6. Fusion and Minimum Wavelength Mapping
Due to the differing survey distances (cf. Figure 5), the swath from Flight A covered
a wider extent than Flight B, but at lower resolution. Thus, after applying the radiometric
and geometric corrections described in Sections 3.4 and 3.5, they were fused to derive a
combined dataset with optimal spatial coverage and resolution. The spectra from each
swath were projected onto two separate hyperclouds using a filtered mapping matrix M
f
that back-projects the smallest-footprint pixel onto each point in the SfM-MVS cloud (cf.
Section 3.3). Then, the per-point spectra from each swath were averaged using a weighting
factor proportional to each pixel’s ground-sampling distance, such that the two surveys
were averaged to combine their extent and (slightly) reduce spectral noise, but without
significantly compromising spatial resolution.
Finally, a hull-correction from 2100–2500 nm was applied to this merged hypercloud
to emphasise absorption features related to clay and mica minerals in the shales (~2200
nm), calcite (~2345 nm) and dolomite (~2325 nm) and create the false-colour composite
presented in Section 4. Minimum wavelength mapping of absorption feature position and
depth (cf. [38–41]) was then performed using the multi-gaussian absorption feature-fitting
algorithm implemented in hylite. This was used to simultaneously fit the three main min-
eralogical absorption features (AlOH, FeOH and CO
3
) expected in the range 2150–2360
nm, and thus allow discrimination between the different minerals at Passo Giau.
3.7. Collection of Validation Spectra
Point spectra measurements were conducted using a portable spectrometer to pro-
vide ground truth data for validating the remotely sensed spectra. Ten sampling sites
were selected based on an on-site evaluation of the geology and stratigraphy, access lim-
itations and to ensure spatial coverage of the extent of the outcrop. Ten measurements
were collected at each of these sites, covering the range of dominant facies or micro-facies
(as interpreted by the geologist), using a FieldSpec spectrometer equipped with a contact

Remote Sens. 2022, 14, 5 13 of 22
probe that allowed measurements of the reflectance spectra from areas of ~1 cm2. These
sampling si tes were the n marked using spra y-on chalk such tha t they could be easi ly iden-
tified in the photogrammetric model of the area. Of these sites, 5 overlapped with the
extent of the UAV survey and could be identified in the photogrammetric point cloud,
allowing equivalent spectra to be sampled from within ±1 m of the spray-on chalk markers
and within the relevant microfacies (as identified based on field observations and inter-
pretation of the photogrammetric model).
4. Results
The geometric correction and boresight optimisation routine described in Sections
3.3 and 3.4 allowed successful correction and georeferencing of the raw hyperspectral
swaths (Figure 8), despite collection under adverse conditions (significant wind and poor
GPS signal) to produce a geometrically and radiometrically corrected hypercloud. Jupyter
notebooks showing the application of these methods are available in the Supplementary
Material.
The resulting point-spectra closely match those measured in situ using the FieldSpec
(Figure 9). The Mjolnir and FieldSpec spectra were compared by calculating the mean av-
erage error (MAE; sensitive to spectra shape and magnitude) and spectral angle (SA; sen-
sitive to spectra shape only) between the Mjolnir and closest FieldSpec spectra at each of
the reference sites (Figure 10). These suggest that the majority of the remotely sensed
Mjolnir spectra are accurate to 2–4% (MAE) and 3.5–5.5° (SA). Considering the significant
challenges associated with remotely sensing accurate reflectance spectra, we consider
these errors to be acceptable.
Figures 9 and 10 also show that the Mjolnir and FieldSpec spectra consistently iden-
tify variations in the position and depth of the AlOH and CO3 absorption features (at
~2200 and 2320–2345 nm, respectively). These subtle differences were be used to map the
dominant mineralogy across the outcrop, and so discriminate the fine-grained dolostone
of the Cassian Dolomite Formation (DCS; Figure 9) from the interbedded micritic lime-
stone and marls of the San Cassiano Formation (SCS; Figure 9), and their dolomite equiv-
alents (SCS-D; Figure 9). SCS and SCS-D both show an AlOH feature indicating the pres-
ence of clay minerals, while the position of the CO3 feature distinguishes calcitic SCS (2345
nm) from dolomitic SCS-D and DCS (2325 nm).
The false colour visualisation (Figure 11a) created using the absorption depth at 2200,
2320 and 2340 nm highlights these differences, clearly distinguishing dolostone (green)
from limestone (blue), as well as shale-rich argillites (purple to red). Similarly, the mini-
mum wavelength map shows variations in the position of the CaCO3 absorption feature
from 2320 nm (dolomite) to 2340 nm (calcite), providing a visualisation of the calcite to
dolomite ratio (Figure 11b). The calcitic limestone (2340–2345 nm) can be clearly distin-
guished from overlying dolomites (2315–2325 nm), and correlates with argillites (San Cas-
siano Fm) identified based on their distinct texture (fine laminations) in the photogram-
metric model and abundant clay or mica minerals identified by mapping the depth of the
2200 nm AlOH absorption feature (Figure 11c).
Interestingly, some of this limestone has been dolomitised to a distance of ~2–5 m
along its top contact (Figure 12), an observation that would likely have been missed if the
unit was mapped using only traditional methods. A geoscience interpretation of these
results is beyond the scope of this current work, but we present some preliminary inter-
pretations in the following section (Section 5). The false colour visualisations and mini-
mum wavelength map results can downloaded from https://tinyurl.com/nuvolau (ac-
cessed on 18 December 2021).

Remote Sens. 2022, 14, 5 14 of 22
Figure 8. True colour uncorrected (a,b) and geometrically corrected (c,d) visualisations of each hy-
perspectral swaths. Although spatial accuracy is limited to ~2 pixels (Figure 5), the extensive geo-
metric distortions in the original images have been almost entirely corrected. The 3-m spaced hori-
zontal contours are overlain in white for reference. Areas in the original images occluded by the leg
of the drone have been masked to prevent projection onto the point-cloud. Note that the duplication
of pixels in the raw hyperspectral swaths means that this did not leave gaps in the geometrically
corrected results.

Remote Sens. 2022, 14, 5 15 of 22
Figure 9. Comparison of corrected HySpex Mjolnir spectra and ground truth FieldSpec measure-
ments (plotted in grey) from the Cassian Dolomite Formation (DCS; green), dolomitised San Cassi-
ano Formation (SCS-D; red), and calcitic San Cassiano Formation (SCS; blue). The location of these
validation sites is plotted in Figures 4 and 11. Note that these measurements were used to check the
spectral accuracy of the UAV data, not to validate a classification or lithological map of the outcrop;
so lithological unit labels provided above are for reference only. Blue vertical lines show the ex-
pected positions of the AlOH, dolomitic CO
3
and calcitic CO
3
absorp tion feat ures mapp ed in Fi gure s
11 and 12.
Figure 10. Quantitative error metrics showing the mean absolute error (a) and spectral angle (b)
between the HySpex Mjolnir and corresponding FieldSpec measurements. Vertical black line shows
the median error and vertical dotted lines the 25th and 75th percentiles. A comparison of the car-
bonate absorption position measured using the different instruments is shown in (c), and shows
that the ~20 nm shift in absorption feature position between calcite and dolomite can be resolved in
both datasets, allowing calcitic lithotypes to be distinguished from dolomitic ones (point colour cor-
responds to the different stratigraphic units; cf. Figure 9). Vertical blue lines show the expected po-
sition for dolomite (2325 nm) and calcite (2345 nm).

Remote Sens. 2022, 14, 5 16 of 22
Figure 11. False-colour visualisation of the merged and radiometrically corrected hypercloud (a)
and minimum wavelength maps showing the distribution of calcitic (blue) and dolomitic (green)
carbonates (b) and clay/mica minerals (purple; c). The location of the validation spectra shown in
Figure 9 are overlain on (c) in yellow for reference.

Remote Sens. 2022, 14, 5 17 of 22
Figure 12. Close-up of the dolostone-limestone contact (dashed white line) showing the stratigraph-
ically crosscutting transition zone containing dolomitised limestone in the true-colour SfM-MVS
model (a), and carbonate (b) and AlOH (c) absorption feature minimum wavelength maps. Note
that the image has been rotated to make bedding sub-horizontal; thin white lines show 3-m spaced
horizontal contour lines. Example reflectance spectra (d) sampled from the coloured circles in (a–c)
are plotted for reference. These spectra (dotted lines) were smoothed (solid lines) using a Savitzky–
Golay filter with a window size of 7 and polynomial order of 2, to reduce noise and highlight the

Remote Sens. 2022, 14, 5 18 of 22
clear shift in absorption position from 2345 nm (calcite) to 2325 nm (dolomite). Refer to Figure 11
for the colour scales used in (b,c).
5. Discussion
Our results highlight the significant potential of UAV-compatible SWIR hyperspec-
tral cameras for creating detailed, accurate and objective geological maps in inaccessible
terrain. As shown in Figures 9–11, the remotely mapped SWIR spectra are accurate and
can distinguish the lithologies and mineral alteration (dolomitisation) at a scale and reso-
lution that would be difficult with ground-based sensors or field mapping. While care
must be taken to fully understand potential weathering effects [42,43], these data signifi-
cantly supplement the high resolution geometric and textural information captured using
conventional digital outcrop mapping techniques, and specifically contribute to provide
a detailed map of mineralogy across the slope to basin transition at Gusela de Nuvolau.
Potential applications of this approach are far broader than mapping carbonate mineral-
ogy in reservoir analogues, and include, e.g., mapping clays, mica and chlorite associated
with hydrothermal alteration around mineral deposits or volcanic edifices for minerals
exploration or geohazard assessment.
A few geological studies have employed UAV hyperspectral SWIR cameras (e.g.,
[14]), but we suggest that significant potential for the development and innovative appli-
cation of both methods remains. We hope that our novel processing approach helps to
realise this potential, by (1) facilitating accurate corrections in areas of steep terrain (e.g.,
cliffs or open pit mines) and (2) retaining true 3-D information by avoiding the need to
project data into a 2-D orthomosaic. The former is significant as areas of exceptional geo-
logical exposure are rarely flat, while the latter avoids gridding and reprojection artefacts
associated with DEM-based workflows (cf. [23]), as real pixel footprints are retained in M,
and also results in point cloud data suited for digital outcrop analysis (e.g., LIME, Cloud-
Compare, VRGS; [32,44,45]) or 3-D modelling.
To demonstrate this, we used VRGS [45] to perform a preliminary interpretation of
the hyperspectral mineral mapping results and associated SfM-MVS point cloud (Figure
13). This synthesises the mineral-mapping results with high resolution textural infor-
mation captured by the SfM-MVS model and regional knowledge to help better constrain
the stratigraphic nature of the slope to basin relationship (e.g., slope breccia downlapping
on basin wedge or interfingering), as well as the spatial reach of the dolomitisation fluids
from coarse slope sediments to basinal, thin bedded, fine-grained limestone intervals al-
ternating with shales and marls (Figure 13). Part of the dolomitising fluids are interpreted
here to have circulated throughout faults and connected fractures (Figure 13), laterally
invading porous and permeable rock volumes. Adjacent non-porous and impermeable
basinal facies acted as a barrier to dolomitising fluids, preserving the associated rock type
from dolomitisation. Dolomitisation processes are complex and multiphase in the Dolo-
mites region, and are not in the scope of this study. Instead, this contribution proves the
reliability of the developed technology and workflow, opening opportunities to acquire
similar, unbiased and accurate, geometrical and mineralogical information along vertical
outcrops, with major implications for the evaluation of subsurface analogues for geother-
mal, CO2 sequestration and conventional hydrocarbon exploration and production.
However, as with any remote sensing technology employing UAVs, data acquisition
is particularly sensitive to field conditions (e.g., weather and GPS signals). The high winds
and poor GPS reception we experienced during data acquisition emphasise the need for
robust processing solutions that can accurately correct data from an erratically moving
platform. Our novel geometric correction and boresight optimisation routines are a step
in this direction, and allowed us to extract accurate data in spite of the adverse conditions,
but there remains significant room for improvement. Tools for planning automated UAV
surveys that use non-nadir camera angles (due to topographic complexity) are also lack-
ing, especially if high-resolution topographic information is unavailable. Small-scale com-
mercial solutions are beginning to emerge from the engineering inspection industry

Remote Sens. 2022, 14, 5 19 of 22
(Skydio; www.skydio.com, accessed on 18 December, 2021), but these currently cannot be
employed for high-payload drones.
Finally, we suggest that good quality analysis and control (QAQC) procedures that
can be applied while in the field to ensure sufficient data quality are needed. Some pro-
gress has been made on applying geometric corrections in real-time [46], but more needs
to be done to make these solutions available, robust and field-ready. Appropriate field
QAQC is essential for many geological studies, as field locations are often inaccessible and
visiting multiple times is impractical or prohibitively expensive. Real-time processing ca-
pabilities also allow adaptation of survey plans and acquisition parameters when areas of
interest are identified, and potentially validated or ground-truthed while onsite. By im-
plementing our method in the open-source hylite toolbox we hope to facilitate advances
in these directions to realise the potential of rapidly developing UAV hyperspectral im-
aging technologies.
Figure 13. Digital outcrop interpretation of the Gusela del Nuvolau cliff based on the SfM-MVS
model and hyperspectral survey data. The minimum wavelength map (cf. Figures 11 and 12) has
been overlain in (a) as a reference. This clearly shows the gradational and stratigraphically

Remote Sens. 2022, 14, 5 20 of 22
crosscutting dolomitisation of the otherwise calcitic San Cassiano Formation (b), and that the Cas-
sian Dolomite Formation has been completely dolomitised.
6. Conclusions
We provide an open-source toolbox for the accurate correction of non-nadir
pushbroom hyperspectral data. Our robust algorithm allows the processing of VNIR and
SWIR data acquired obliquely and targeting outcrops with complex geometries, such as
cliffs or mines. In a first step, this algorithm was developed for the Hyspex NEO Mjolnir,
but we expect users to be able to use our tools to process data from any pushbroom sensor,
provided a detailed photogrammetric outcrop model is also available. The procedure is
included in the open-source hylite package (https://github.com/samthiele/hylite, accessed
on 18 December 2021), and the authors welcome further contributions. We demonstrated
the potential of this toolbox using data collected in conditions representative of opera-
tional acquisitions. Despite non-optimal weather and GPS reception, distortions and re-
lated artefacts could be successfully corrected to derive a coherent and spectrally accurate
dataset. This contribution opens the door to new UAV hyperspectral acquisition types
and supports the development of UAV-based imaging in Earth sciences.
Supplementary Materials: The following are available online at www.mdpi.com/arti-
cle/10.3390/rs14010005/s1, S1: Agisoft Metashape processing report, N1: IMU Visualisation note-
book, N2a: Flight A geometric corrections notebook, N2b: Flight B geometric corrections notebook,
N3a: Flight A illumination corrections notebook, N3b: Fight B illumination corrections notebook,
N4: Data fusion notebook, N5: Minimum wavelength mapping notebook
Author Contributions: Conceptualisation by R.G., J.K., S.T.T., E.D. and S.L.; methodology, S.T.T.,
Z.B. and S.L.; software, S.T.T., Z.B. and Y.M.; validation, S.T.T., N.M., A.B., E.D. and J.K.; writing—
original draft preparation, S.T.T., S.L., Z.B., A.B. and N.M.; writing—review and editing, S.T.T., S.L.,
A.B., R.G., E.D. and J.K.; project administration, R.G. and J.K.; funding acquisition, R.G. and J.K. All
authors have read and agreed to the published version of the manuscript.
Funding: The authors would like to acknowledge funding from TotalEnergies, The European Social
Fund (ESF) and the State of Saxony.
Data Availability Statement: Data presented in this study are available at
https://doi.org/10.6084/m9.figshare.17126321.v1, accessed on 18 December 2021.
Conflicts of interest: The authors declare no conflict of interest.
References
1. Bemis, S.; Micklethwaite, S.; Turner, D.; James, M.R.; Akciz, S.; Thiele, S.T.; Bangash, H.A. Ground-based and UAV-Based pho-
togrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology. J. Struct. Geol. 2014, 69,
163–178. https://doi.org/10.1016/j.jsg.2014.10.007.
2. Dering, G.M.; Micklethwaite, S.; Thiele, S.T.; Vollgger, S.A.; Cruden, A.R. Review of drones, photogrammetry and emerging
sensor technology for the study of dykes: Best practises and future potential. J. Volcanol. Geotherm. Res. 2019, 373, 148–166.
https://doi.org/10.1016/j.jvolgeores.2019.01.018.
3. Janiszewski, M.; Uotinen, L.; Merkel, J.; Leveinen, J.; Rinne, M. Virtual Reality Learning Environments for Rock Engineering,
Geology and Mining Education. In Proceedings of the 54th U.S. Rock Mechanics/Geomechanics Symposium (ARMA-2020-
1101), Golden, Colorado, USA, 28 June–1 July 2020.
4. Nesbit, P.; Durkin, P.R.; Hugenholtz, C.H.; Hubbard, S.; Kucharczyk, M. 3-D stratigraphic mapping using a digital outcrop
model derived from UAV images and structure-from-motion photogrammetry. Geosphere 2018, 14, 2469–2486.
https://doi.org/10.1130/ges01688.1.
5. Marques, A.; Horota, R.K.; de Souza, E.M.; Kupssinskü, L.; Rossa, P.; Aires, A.S.; Bachi, L.; Veronez, M.R.; Gonzaga, L.; Cazarin,
C.L. Virtual and digital outcrops in the petroleum industry: A systematic review. Earth-Sci. Rev. 2020, 208, 103260.
https://doi.org/10.1016/j.earscirev.2020.103260.
6. Laukamp, C.; Rodger, A.; LeGras, M.; Lampinen, H.; Lau, I.; Pejcic, B.; Stromberg, J.; Francis, N.; Ramanaidou, E. Mineral Phys-
icochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal
Infrared Reflectance Spectra. Minerals 2021, 11, 347. https://doi.org/10.3390/min11040347.
7. Kurz, T.H.; Buckley, S.J.; Howell, J.A. Close-range hyperspectral imaging for geological field studies: Workflow and methods.
Int. J. Remote Sens. 2013, 34, 1798–1822. https://doi.org/10.1080/01431161.2012.727039.

Remote Sens. 2022, 14, 5 21 of 22
8. Kurz, T.H.; Buckley, S.J.; Howell, J.A.; Schneider, D. Integration of panoramic hyperspectral imaging with terrestrial lidar data.
Photogramm. Rec. 2011, 26, 212–228. https://doi.org/10.1111/j.1477-9730.2011.00632.x.
9. Lorenz, S.; Salehi, S.; Kirsch, M.; Zimmermann, R.; Unger, G.; Vest Sørensen, E.; Gloaguen, R. Radiometric Correction and 3D
Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops. Remote Sens.
2018, 10, 176. https://doi.org/10.3390/rs10020176.
10. Thiele, S.T.; Lorenz, S.; Kirsch, M.; Acosta, I.C.C.; Tusa, L.; Herrmann, E.; Möckel, R.; Gloaguen, R. Multi-scale, multi-sensor
data integration for automated 3-D geological mapping. Ore Geol. Rev. 2021, 136, 104252.
https://doi.org/10.1016/j.oregeorev.2021.104252.
11. Aasen, H.; Honkavaara, E.; Lucieer, A.; Zarco-Tejada, P.J. Quantitative Remote Sensing at Ultra-High Resolution with UAV
Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens. 2018,
10, 1091. https://doi.org/10.3390/rs10071091.
12. Kim, J.; Chi, J.; Masjedi, A.; Flatt, J.E.; Crawford, M.M.; Habib, A.F.; Lee, J.; Kim, H. High-resolution hyperspectral imagery from
pushbroom scanners on unmanned aerial systems. Geosci. Data J. 2021. https://doi.org/10.1002/gdj3.133.
13. Goldstein, N.; Wiggins, R.; Woodman, P.; Saleh, M.; Nakanishi, K.; Fox, M.E.; Tannian, B.E.; Ziph-Schatzberg, L.; Soletski, P.
Compact visible to extended-SWIR hyperspectral sensor for unmanned aircraft systems (UAS). In Proceedings of the Algo-
rithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, Bellingham, Washington DC, USA,
17–19 April 2018; Volume 10644, p. 106441G.
14. Barton, I.F.; Gabriel, M.J.; Lyons-Baral, J.; Barton, M.D.; Duplessis, L.; Roberts, C. Extending geometallurgy to the mine scale
with hyperspectral imaging: A pilot study using drone- and ground-based scanning. Min. Met. Explor. 2021, 38, 799–818.
https://doi.org/10.1007/s42461-021-00404-z.
15. Arroyo-Mora, J.; Kalacska, M.; Inamdar, D.; Soffer, R.; Lucanus, O.; Gorman, J.; Naprstek, T.; Schaaf, E.; Ifimov, G.; Elmer, K.;
et al. Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring. Drones 2019, 3, 12.
https://doi.org/10.3390/drones3010012.
16. Booysen, R.; Jackisch, R.; Lorenz, S.; Zimmermann, R.; Kirsch, M.; Nex, P.A.M.; Gloaguen, R. Detection of REEs with lightweight
UAV-based hyperspectral imaging. Sci. Rep. 2020, 10, 17450. https://doi.org/10.1038/s41598-020-74422-0.
17. Kirsch, M.; Lorenz, S.; Zimmermann, R.; Tusa, L.; Möckel, R.; Hödl, P.; Booysen, R.; Khodadadzadeh, M.; Gloaguen, R. Integra-
tion of Terrestrial and Drone-Borne Hyperspectral and Photogrammetric Sensing Methods for Exploration Mapping and Min-
ing Monitoring. Remote Sens. 2018, 10, 1366. https://doi.org/10.3390/rs10091366.
18. JuanManuel, J.R.; Padua, L.; Hruska, J.; Feito, F.R.; Sousa, J.J. An Efficient Method for Generating UAV-Based Hyperspectral
Mosaics Using Push-Broom Sensors. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 6515–6531.
https://doi.org/10.1109/jstars.2021.3088945.
19. Turner, D.; Lucieer, A.; McCabe, M.; Parkes, S.; Clarke, I. Pushbroom Hyperspectral Imaging from an Unmanned Aircraft Sys-
tem (UAS)—Geometric Processingworkflow and Accuracy Assessment. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
2017, XLII-2/W6, 379–384. https://doi.org/10.5194/isprs-archives-xlii-2-w6-379-2017.
20. Angel, Y.; Turner, D.; Parkes, S.; Malbeteau, Y.; Lucieer, A.; McCabe, M.F. Automated Georectification and Mosaicking of UAV-
Based Hyperspectral Imagery from Push-Broom Sensors. Remote Sens. 2020, 12, 34. https://doi.org/10.3390/rs12010034.
21. Schlaepfer, D.; Schaepman, M.E.; Itten, K.I. PARGE: Parametric geocoding based on GCP-calibrated auxiliary data. SPIE’s Int.
Symp. Opt. Sci. Eng. Instrum. 1998, 3438, 334–344. https://doi.org/10.1117/12.328114.
22. Ghamisi, P.; Shahi, K.R.; Duan, P.; Rasti, B.; Lorenz, S.; Booysen, R.; Thiele, S.; Contreras, I.C.; Kirsch, M.; Gloaguen, R. The
Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials. IEEE J. Sel. Top. Appl. Earth Obs.
Remote Sens. 2021, 14, 8971–8988. https://doi.org/10.1109/jstars.2021.3108049.
23. Inamdar, D.; Kalacska, M.; Arroyo-Mora, J.P.; Leblanc, G. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving
the Integrity of Hyperspectral Imaging Data. Front. Remote Sens. 2021, 2, 9. https://doi.org/10.3389/frsen.2021.675323.
24. Buckley, S.; Kurz, T.H.; Howell, J.A.; Schneider, D. Terrestrial lidar and hyperspectral data fusion products for geological out-
crop analysis. Comput. Geosci. 2013, 54, 249–258. https://doi.org/10.1016/j.cageo.2013.01.018.
25. Bosellini, A. Progradation geometries of carbonate platforms: Examples from the Triassic of the Dolomites, northern Italy. Sed-
imentology 1984, 31, 1–24. https://doi.org/10.1111/j.1365-3091.1984.tb00720.x.
26. Neri, C.; Gianolla, P.; Furlanis, S.; Caputo, R.; Bosellini, A. Note Illustrative Della Carta Geologica d’Italia. In Foglio Cortina
D’ampezzo 029; ISPRA: Rome, Italy, 2007.
27. Inama, R.; Menegoni, N.; Perotti, C. Syndepositional fractures and architecture of the lastoni di formin carbonate platform:
Insights from virtual outcrop models and field studies. Mar. Pet. Geol. 2020, 121, 104606. https://doi.org/10.1016/j.mar-
petgeo.2020.104606.
28. Blendinger, W.; Blendinger, E. Windward-leeward effects on Triassic carbonate bank margin facies of the Dolomites, northern
Italy. Sediment. Geol. 1989, 64, 143–166. https://doi.org/10.1016/0037-0738(89)90089-4.
29. Bosellini, A.; Neri, C. The Sella Platform. Dolomieu Conference on Carbonate Platforms and Dolomitization; Guidebook Excursion B;
KARO-Druck: Ortisei, Italy, 1991.
30. Cadrobbi, L.; Nobile, G.; Lutterotti, G. Studio Di Supporto Alla Stesura Del Piano Regolatore Generale Del Comune Di Canazei, Studio
Associato Di Geologia Applicata; Canazei, Italy, 1995; Volume 1955-1.
31. Mollema, P.N.; Antonellini, M. Development of strike-slip faults in the dolomites of the Sella Group, Northern Italy. J. Struct.
Geol. 1999, 21, 273–292. https://doi.org/10.1016/s0191-8141(98)00121-7.

Remote Sens. 2022, 14, 5 22 of 22
32. Girardeau-Montaut, D. CloudCompare. 2020. Available online: https://cloudcompare.org/ (accessed on 18 December 2021).
33. Akenine-Moller, T.; Haines, E.; Hoffman, N. Real-Time Rendering; AK Peters/crc Press: Natick, MA, USA, 2019; ISBN 1-315-
36545-6.
34. Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.;
Bright, J.; et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 2020, 17, 261–272.
https://doi.org/10.1038/s41592-019-0686-2.
35. Thiele, S.T.; Lorenz, S.; Kirsch, M.; Gloaguen, R. A Novel and Open-Source Illumination Correction for Hyperspectral Digital
Outcrop Models. IEEE Trans. Geosci. Remote Sens. 2021, 14, 1–12. https://doi.org/10.1109/tgrs.2021.3098725.
36. Oren, M.; Nayar, S.K. Seeing beyond Lambert’s law. In Proceedings of the Computer Vision—ECCV ’94; Springer: Berlin/Heidel-
berg, Germany, 1994; pp. 269–280.
37. Berthaume, M.A.; Winchester, J.; Kupczik, K. Ambient occlusion and PCV (portion de ciel visible): A new dental topographic
metric and proxy of morphological wear resistance. PLoS ONE 2019, 14, e0215436. https://doi.org/10.1371/journal.pone.0215436.
38. Huguenin, R.L.; Jones, J.L. Intelligent information extraction from reflectance spectra: Absorption band positions. J. Geophys.
Res. Space Phys. 1986, 91, 9585–9598. https://doi.org/10.1029/jb091ib09p09585.
39. Van der Meer, F. Analysis of spectral absorption features in hyperspectral imagery. Int. J. Appl. Earth Obs. Geoinf. 2004, 5, 55–68.
https://doi.org/10.1016/j.jag.2003.09.001.
40. Van Ruitenbeek, F.; Bakker, W.H.; van der Werff, H.; Zegers, T.E.; Oosthoek, J.H.; Omer, Z.A.; Marsh, S.; van der Meer, F.D.
Mapping the wavelength position of deepest absorption features to explore mineral diversity in hyperspectral images. Planet.
Space Sci. 2014, 101, 108–117. https://doi.org/10.1016/j.pss.2014.06.009.
41. Sunshine, J.; Pieters, C.M.; Pratt, S.F. Deconvolution of mineral absorption bands: An improved approach. J. Geophys. Res. Space
Phys. 1990, 95, 6955–6966. https://doi.org/10.1029/jb095ib05p06955.
42. Beckert, J.; Vandeginste, V.; McKean, T.J.; Alroichdi, A.; John, C.M. Ground-based hyperspectral imaging as a tool to identify
different carbonate phases in natural cliffs. Int. J. Remote Sens. 2018, 39, 4088–4114.
https://doi.org/10.1080/01431161.2018.1452068.
43. Kurz, T.H.; San Miguel, G.; Dubucq, D.; Kenter, J.; Miegebielle, V.; Buckley, S.J. Quantitative Mapping of Dolomitization Using
Close-Range Hyperspectral Imaging: Kimmeridgian Carbonate Ramp (Alacón, NE Spain). Geosphere 2022, in press.
44. Buckley, S.J.; Ringdal, K.; Naumann, N.; Dolva, B.; Kurz, T.H.; Howell, J.A.; Dewez, T.J. LIME: Software for 3-D visualization,
interpretation, and communication of virtual geoscience models. Geosphere 2019, 15, 222–235. https://doi.org/10.1130/ges02002.1.
45. Hodgetts, D.; Gawthorpe, R.L.; Wilson, P.; Rarity, F. Integrating Digital and Traditional Field Techniques Using Virtual Reality
Geological Studio (VRGS). In Proceedings of the 69th EAGE Conference and Exhibition Incorporating SPE EUROPEC 2007; EAGE
Publications BV: Utrecht, The Netherlands, 2007; p. cp-27-00300.
46. Koirala, P.; Løke, T.; Baarstad, I.; Fridman, A.; Hernandez, J. Real-time hyperspectral image processing for UAV applications,
using HySpex Mjolnir-1024. In Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Im-
agery XXIII; SPIE: Bellingham,WA, USA; Washington DC, USA, 2017; Volume 10198, p. 1019807.