Forum Geografi, 31(1), 2017; DOI: 10.23917/forgeo.v31i1.3977
Photogrammetry-based Texture
Analysis of a Volcaniclastic Outcrop-peel: Low-cost
Alternative to TLS and Automation Potentialities using Haar
Wavelet and Spatial-Analysis Algorithms
1 Graduate School of Maritime Sciences, Kobe
University, Higashinada-ku, Fukae-Minamimachi
5-1-1, 658-0022 Kobe City, Japan
2 Niigata University, Research Institute for Natural Hazards & Disaster Recovery. 950-2181 Niigata-City, Nishi-ku, Ikarashi 2nochou 8050, Japan
3 College of Sciences, Department of Geography, University
of Canterbury Christchurch New Zealand
4 Faculty of Geography, Universitas
Muhammadiyah Surakarta
5 Department of Environment, University of
Strasbourg
6 Paris 1 Sorbonne University LGP Laboratory,
Paris, France.
7 Faculty of Horticulture, Chiba University,
Matsudo-city, Chiba-ken, Japan.
*) Corresponding author (e-mail: christophergomez@bear.kobe-u.ac.jp)
Received: 01 May 2017 / Accepted:
19 June 2017 / Published: 01 July 2017
Abstract
Numerous progress has been made in the field of applied photogrammetry
in the last decade, including the usage of close-range photogrammetry as a mean
of conservation and record of outcrops. In the present contribution, we use the
SfM-MVS method combined with a wavelet decomposition
analysis of the surface, in order to
relate it to morphological and surface roughness data. The results demonstrated
that wavelet decomposition and RMS could provide
a rapid insight on the location of coarser materials and individual outliers,
while arithmetic surface roughness were
more useful to detect units or layers that are similar on the outcrop. The
method also emphasizes the fact that the
automation of the process does not allows
clear distinction between any artefact
crack or surface change and that human supervision is still essential despite
the original goal of automating the outcrop surface analysis.
Keywords: Structure-from-Motion,
Multiple-View Stereophotogrammetry, Wavelet-decomposition,
texture analysis, surface roughness, GIS; Spatial Analysis, Close-range Remote
Sensing.
Abstrak
Banyak kemajuan telah dicapai di bidang fotogrametri terapan dalam kurun dekade
terakhir, termasuk penggunaan fotogrametri jarak dekat sebagai
rujukan konservasi dan perekaman singkapan
batuan. Dalam kontribusi sekarang, kami menggunakan metode SfM-MVS yang dikombinasikan dengan analisis dekomposisi wavelet terhadap permukaan lahan, untuk menghubungkannya dengan data morfologi dan kekasaran permukaan.
Hasilnya menunjukkan bahwa dekomposisi wavelet dan RMS dapat memberikan
wawasan yang cepat mengenai lokasi bahan kasar dan
outlier individu, sedangkan
aritmatika kekasaran permukaan lebih berguna untuk mendeteksi
unit atau lapisan yang serupa pada singkapan
batuan. Metode ini juga menekankan fakta bahwa otomatisasi
proses tidak memungkinkan perbedaan yang jelas antara retakan artefak atau perubahan
permukaan dan bahwa pengawasan manusia masih penting
meskipun tujuan awalnya mengotomatisasi analisis permukaan singkapan.
Kata kunci: Structure-from-Motion, stereophotogrammetry multi sudut pandang, dekomposisi wavelet, analisis tekstur, kekasaran permukaan, SIG, analisis spasial, Penginderaan jauh jarak dekat.
Introduction
Science is a product of its time, its imperatives, the
technological advances allowing one type of work over another, sometimes the
fascination of human for one technology or another. Consequently science can’t
be fully separated from its social receptacle. As population in countries like
Japan are thinning, and that such dwingling always appear after a peak, there
is a recurrent pattern of “how do we keep what we used to do, but with less
manpower”. Such question is meant to come in 40 to 50 years in Indonesia as
well.
One way of addressing this issue is to increase
automation. In Geology, it means using various tools to employ a machine to do
what used to be done by hand. It is within this conceptual framework that the
authors have built the present contribution. The aim has therefore been to
start developing a method that autmoates some of the outcrop analysis process.
In a country, like Indonesia, where population is plenty, this research is also
aimed at improving crowd-sourcing automated analysis information.
Outcrops are arguably one of the most important sources
of data to study the geology and the geomorphology of volcanic environments. Outcrop
analysis is essential – for instance – for calibrating several non-destructive
methods, such as ground-penetrating radar (GPR), in volcanic environments where
data collection can be challenging (Abrams and
Sigurdsson, 2007; Cassidy et al., 2009; Courtland et al., 2012; Finizola
et al., 2010; Gomez
and Lavigne, 2010; Gomez et al., 2008, 2009, 2012; Gomez-Ortiz et al., 2006; Khan
et al., 2007; Lavigne et al.,
2007). Despite being a traditional technique, outcrop analysis has recently
seen a methodological resurgence with the application of remote sensing (RS) techniques,
such as close-range hyperspectral imagery to map mineral content (Buckley et al., 2013) and terrestrial laser
scanning, to describe millimeter to centimeter scale features (Bellian et al.,
2005). Exploration using these RS techniques has been relatively sparse,
most probably because the technical and financial aspects are still
prohibitive, and the great majority deals with sub-horizontal surfaces rather
than sub-vertical ones (e.g. Heritage and Milan, 2009).
Moreover, the contributions – to date – deal mostly with data acquisition and
handling rather than obtaining parameters from which one could derive
indicators on the nature of the studied material in an automated manner (e.g. Giaccio
et al., 2002).
One potential direction that can be explored is the
analysis of outcrop surface texture (or surface roughness), which can be a key proxy for environmental processes. This is
particularly true in the field of agriculture where surface roughness gives
indications on wind deflation, runoff and water absorption, even playing an
important part in soil biota and gas exchanges (Vidal
Vazquez et al., 2005). Geologists
have also used variations of surface texture to characterize different volcanic
deposits (Bretar et al., 2013) and the mechanisms of rock fragmentation (Tatone and Grasselli, 2009). Surface texture (whether
formed by erosion or deposition) also controls electromagnetic scattering on a
surface and therefore plays an important role in remote sensing interpretation
(Beckmann and Spizzichino, 1987). In this paper, we
add to the recent research on using remote-sensing techniques for outcrop analysis. As a key to enter
this problem, we explore in the present contribution the question of whether
cost-effective remote sensing data acquisition can be used to accurately
describe surface texture of volcanic outcrops.
In this paper, we: (1) present Structure–from-Motion
associated with Multiple-View Stereophotogrammetry (SfM-MVS), a low-cost
alternative to terrestrial laser scanning (Morgenroth and Gomez, 2014) and
describe how it could be applied to outcrop-scale analysis; and (2) test
various surface texture indicators and the use of wavelet decomposition for
surface roughness analysis, in order to determine if these indicators could be
used for automatic recognition of granularity and derivation of grain-size
variations.
In
1979, Structure-from-Motion (also known as Structure-and-Motion) was first developed in the field of
computer-vision engineering (Ullman, 1979). It has
since developed into a valuable tool for generating 3D models from 2D imagery (Szelinski, 2011), notably with the development of
software with Graphical User Interfaces. Traditional photogrammetry requires a
series of identifiable points to be present in at least two photographs and,
perhaps more importantly, known values of camera projection, distortion,
position, and orientation (Robertson and Cipolla, 2009).
By contrast, SfM uses algorithms to identify matching features in a collection
of overlapping digital images, and calculates camera location and orientation
from the differential positions of multiple matched features (Fisher, et al.,
2005; Quan, 2010; Szeliski,
2011). Based on these calculations overlapping imagery can be used to
reconstruct a 3D model of the photographed object or scene. Where relative
projection geometry and camera position are known the values can be integrated
into the SfM reconstruction to improve the calculation productivity and
accuracy of the model (Agisoft
Photoscan-PRO, 2012).
This
study used a commercial software program, Agisoft PhotoScan®-Professional
(Agisoft LLC, St. Petersburg, Russia). Although the procedures described in
this study are achievable using various free-ware options, the decision to use PhotoScan-Professional
software was made because it couples SfM technology with multi-view
stereophotogrammetry (MVS) algorithms in a user-friendly interface. Using this
combined SfM-MVS approach, the software retrieves an initial set of sparse
points from matching features (SfM) and then increases the point-cloud density
to improve the reconstruction of the overlying 3D mesh using MVS technology
(Agisoft Photoscan-PRO, 2012;
James and Robson, 2012; Verhoeven,
et al., 2012).
In order to numerically study the variations of a
surface from an ideal general shape, a series of tools are available, spanning
from descriptive statistical indicators to more complex fractal-based (Bretard, et al.,
2013) and wavelet-based analysis (Gomez, 2012)
allowing measures at various scales (Gomez, 2013).During
the last 10 years, the use of wavelet analysis in earth-sciences has increased
concomitantly with the increasing availability of numerical data. It has
especially benefited from the study of time-series for the determination of
different frequencies and momentums (e.g. Andreo et al.,
2006; Partal and Küçük 2006; Rossi et al., 2009).
Analyses of space-scale data with wavelet - although more scarce in
earth-sciences – are also on the rise (e.g. Audet and
Mareschal, 2007; Booth et al., 2009; Lashermes et al., 2007), eventually following
the influence of research in medical imagery, which has been widely using
wavelet for topographical analysis for instance (e.g. Langenbucher et
al., 2002).
Wavelets allow the decomposition of a signal into a set
of approximations, which is hierarchically organized in a combination of
different scales. Wavelet analyses use a short-term duration wave as a kernel
function in an integral transform. There are several types of wavelet, which
are named after their inventors: e.g. Morlet wavelet, Meyer wavelet. Based on
the shape of the series/function that needs to be analyzed, the appropriate
mother wavelet is scaled and translated (daughter wavelet), allowing the
detection of the different frequencies of a signal at different time (Torrence and Compo 1998; Schneider and Farge, 2006). This
mathematical transform can be very useful to study surface variations of
large-scale topography or localized surface texture Wavelet is a well-fitted tool for separating spectral
components of topography (i.e. working on different scales of a single object),
because it gives both the spatial and the spectral resolution. Consequently, it
is a mathematical tool that reproduces some of the abstraction that human being
do when looking at an outcrop or any other object, separating the different
scales to make sense of the objects they are linked to.
Research Method
Location
Japan is a volcanic archipelago that seats on the Pacific Ring of Fire
and it is arguably one of the most tectonically and volcanically active regions
in the world. Numazawa Volcano is located on the main
island of Japan, Honshu (Figure 1), in the western part
of Fukushima Prefecture and about 50km west of the volcanic front (Yamamoto, 2007; Kataoka et al.,
2008). Numazawa Volcano (835m a.s.l.
at the peak Maeyama) has developed on the edge of the Uwaigusa
caldera complex (Yamamoto and Komazawa,
2004). The volcano encompasses a caldera lake in 2km diameter and ~100m
deep at the level about 475m a.s.l. Chronologically,
the volcaniclastic deposits generated by Numazawa volcano are: the 110k.a. Shibahara pyroclastic
deposits; the 71 k.a Mukuresawa
lava dome; the ~50k.a Mizunuma pyroclastic-flow
deposits; the Sozan lava dome of 43 k.a; the Maeyama lava dome of 20k.a and the Numazawako eruption of 5.4k.a (Yamamoto,
2007). Kataoka et al. (2008) have described in details an
outburst flood from a temporal dam lake formed in the Tadami
River valley by the Numazawako ignimbrite emplacing eruption
and its geomorphic impacts around the volcano, including the flood terraces in
the Tadami River, from which the material used in the
present contribution has been extracted (see Figure 9 in Kataoka et al.,
2008). The 290cm high x 85cm wide outcrop-peel was extracted from the flood
deposits with multiple inversely graded bed sets, rich in rounded pumices
indicative of a hyperconcentrated flow deposition (Kataoka et
al., 2008). The deposit is dominated by coarse sand to pebble
size material. The peel is part of a 15m thick unit that lied on top of
debris-flow deposits.
Data Collection and Analysis
For the present study, a sandy to gravely material from
Numazawa Volcano (Japan) has been digitally acquired and analyzed. The digital
data has been collected using a point and shoot digital camera (Canon
cybershot), by ‘hovering’ over the outcrop taking 170 photographs from a
distance of 10 to 40cm. The method for image acquisition may differ depending on the algorithm used (e.g. Figure 1 in Westoby, 2012). In this study, photographs were taken to
maximize the overlap such that features of the outcrop were captured by multiple
photographs.
Using PhotoScan – professional, we applied the SfM
technique to reconstruct a point-cloud based solely on the uncalibrated
photographs, with tie-points of known location (x,y,z) in order to constrain
the point-cloud in 3D. We subsequently used the MVS technique to
build a 3D surface from the 3D point-cloud
and camera location calculated by SfM. The 3D
mesh was exported as both a vector model and a pixel based map.
Data were then
exported into (1) the GIS environment ArcGIS® (ESRI,
Redlands, CA, USA) and (2) the MATLAB® (MathWorks,
Inc, Natick, MA, USA) programming environment. In the GIS environment, the 3D
surface created from SfM-MVS was loaded as a single layer and transformed into
a tiff file that can be recognized as a 3 level matrix in Matlab. The dataset was then transferred into the
Matlab programming environment to conduct the examination and measures of
surface texture-variations/roughness using a series of different mathematical
tools: (a) wavelet decomposition; (b) arithmetic average roughness; and (c)
proximity analysis of positive and maximum negative variation in a square of
2x2cm. The algorithms
were implemented using ‘cellular automata-type’ series of scripts. The acquisition
and processing methods have been then discussed to present the limits and
potentials of the different method.
Results
Visual description from 3D digital
outcrop
Using the visual results of the SfM-MVS recomposition,
a series of beds of medium to coarse sands and pumice pebbles have been
identified in the 2.9m interval (Figure 2). These pumice
clasts have a main axis (L) of the range ~10 to 80 mm (as measured from the 3D
digital outcrop) and a visible surface of 8 to 250mm2 (Figure 2). Most individual layers dominated by sand grains and
centimetres to decimeters thick contain pimice pebble and cobble unevenly distributed except for an
isolated single large pumice located at 145 cm height. In the upper half of the outcrop, mostly pumice clasts display
characteristics of L>51 mm located
between 145cm and 290cm height, and only 3 clasts of 36mm < L < 50mm are
located in the bottom half of the outcrop. The SfM-MVS visual reconstruction
can therefore yield useful information in terms of distributions (part of
grainsize) and axes orientations of clasts for a traditional outcrop visual
analysis (Cf. visual in Figure 3-a), but more importantly SfM-MVS
also reconstructed the surface ‘vertical topography’ of the outcrop (Figure 3).
Haar-wavelet decomposition as a tool
to study micro-variations
The vertical topography of the outcrop peel derived from SfM-MVS has been tied
on a
perfectly vertical wall, and therefore a slight slant of 6 cm over the
290 cm height of the outcrop-peel appears. In
order to perform localized
analysis of the surface variations, the general slope of the surface has been subtracted using wavelet decomposition (Figure 3-c,d,e). The resulting
variation is shown in Figure
3-e, where only the variations independent
from the general sloping trend have been conserved. This transformation has put
the emphasis of the lower part of the outcrop where numerous micro-topography
variations were disappearing in the general slope acceleration (see the
discussion for the interpretation of this slope). In the upper part of the
outcrop, just above and below the 500 sampling point, one can observe -
in Figure 3-e – the strong
variation of the signal and link them to two units of coarser material
including larger clasts of centimeter-scale (Figure 3-a & Figure 2).
Since the different levels of wavelet decomposition are scale-related,
we have used the lowest level of the Haar-wavelet
decomposition (Figure 4) in
order to detect the finer micro-variations of the outcrop along 7 vertical transects
equally spaced between 10cm and 70cm. This analysis has yielded positive
results with variations in the coarser units being clearly detected in ‘A’, ‘B’
and ‘F’ (Figure 4).
Local inclusions of larger size pumice clasts have
also influenced the signal (Figure 4-C). In the same manner, sandy layers without inclusion of large clasts or pebbles have displayed smoother signal traces with
limited amplitude (Figure 4-D). The signal also reacted to microvariations that are not due to grain-size
variations, but linked to the fracture of the outcrop-peel itself. One
can also observe the desiccation holes and cracks (Figure 4-E)
and those created by peeling and transportation of the outcrop-peel (Figure 4-G). The effects of
the micro-rills located at the bottom of the outcrop – and which did not appear
strongly in the combined levels of the wavelet decomposition (Figure
3-E) – have created strong amplitude variations in the lowest level of the
wavelet decomposition (Figure 4-H).
Wavelet decomposition has shown to be a useful tool to
automate processes such as detrending and surface roughness patterns, but the
reasons behind the signal micro-variations can have various sources limiting an
automated recognition system based solely on wavelet decomposition.
Statistical and Spatial Analysis to detect surface
roughness micro-variations
The indicators used in the present section are normally
used to detect microvariations in GIS and in the manufacturing industry. Although the
instrumentation and the scale are different the underlying algorithms are
similar. The first indicator tested is the arithmetic roughness average (Ra),
which gives indications of the localized maximum variation (Figure
5). This algorithm, computed over an average moving window of 2 cm2
has been successful at identifying rapid
localized variations generated by increased roughness due to the coarseness
of the matrix (Figure 5-b,d).
It also succeeded at identifying smoother material on the outcrop (Figure 5-a,c,e) and defining their
smaller scale variations. Indeed the variation of coarse material is in the
order of x*10-3 m,
while finer material varies in the order of x*10-4
m from local average variation (one will note
that these later variations are below the mm scale and most certainly fall
within the error of margin of data acquisition).
The second algorithm tested with a relative success is the RMS:
It calculates the root mean square of the squared
variation values from an ideal surface – in the present case the detrended
surface.
The result of the RMS shows the ability of the
algorithm to detect and individualize local variations, such as the presence of
the centimeter-scale clast inclusions (Figure 6-1&2), but it also detects quick
variations such as the edge of a layer slightly protruding from the rest of the
outcrop (Figure 6-3).
Discussion and Concluding Remarks
The different algorithms using wavelet and spatial
statistics techniques tested here on the SfM-MVS derived data have shown their
ability to produce measures of the surface roughness. It also shows that the SfM-MVS
is a method that can successfully capture microtopographic
variations on outcrops even at the millimeter
level. The intrinsic advantage of this workflow is its low-cost (e.g. of
topographic application: Westoby et al., 2012) and the fact that anybody can go and collect data
for the scientist to process, as it only requires standard overlapping
photographs from a low-cost, off-the-shelf camera. However, the surface
roughness processing algorithms tested here are not sufficiently developed to automate the process of recognizing layers
of coarser grain-size or the presence of larger clasts in a series as they also
reacts to the imperfections of the outcrop.
Although the detection of these imperfections hamper
the full-automation of the process in the present study, the behavior of these
algorithms could be used to detect the imperfections on rock-faces, which Giaccio et al. (2002) measured using a roughness-meter, and used as a
proxy of erosion features invisible to the naked eye . Although the ‘manual
processing and interpretation’ of data captured with a roughness-meter or even
non-contact methods such as SfM-MVS or TLS is possible, it is necessary to
improve the speed of data processing for large-surface outcrop, outcrop peels
or event potential use on rock faces.
Despite portability, low-cost and
low-logistic demand, SfM-MVS is extremely
time-consuming during the processing process (Westoby et al., 2012). Using a four core E7
at 2GhZ CPU and 6GB RAM the SfM-MVS
process reached almost 30 hours computing. It has been suggested that this
lengthy process could be reduced by diminishing the resolution of the
photographs, though this may result in loss of detail, then in the
density of keypoints and thus, the final
quality of the reconstructed surface may be diminished.
Such suggestion works for work at the landscape scale, but for our study we
were interested by micro-variations and therefore working at a sub-centimeter to centimeter level, it is therefore important
to keep the full-resolution of the photographs, in which case the speed of
processing can only be accelerated using more powerful computers.
The development of this method, the need for powerful
algorithms and the hardware limitation are symptomatic of the recent shift in
the geo-sciences technical paradigms, where data is widely available and easy
to collect (compared to ~30 years ago) and is now less of a challenge than the
processing of data too numerous to be effectively processed in a timely manner. Such a shift and
necessity is also perceptible in the funding
strategies like the late year 2013 NSF grant ‘EarthCube’, aiming to develop
cyber-infrastructure in earth-sciences.
One also has to be careful of the significance of the
data that are collected. Indeed, although the present sediment peel is used as
a support to present a wavelet-based analysis of SfM-MVS collected data, the
results are only significant for a very small portion of an outcrop and they
are also constrained by the technique that was used to create the outcrop.
Indeed some of the topgoraphic features extracted from the peel sample can be
linked to the technique. It is indeed most probable that some of the outcrop
features are due to the outcrop surface that had a slope and that allowed the
glue to penetrate some layers more than others, so that more sediments
“clinged” to the peel.
Finally, SfM-MVS
is a rapid method to collect very fine outcrop data in the field, and could be
extensively used on volcanoes, because it would allow the preservation of
orthorectified and georeferenced outcrop morphologies and images, which would
be extremely useful for comparisons after volcanic evolutions, especially on
volcanoes that change extremely quickly. Such extended dataset are therefore in
need of algorithms providing partial or full-automation of some of the
processing steps. It appears that wavelet decomposition and RMS would provide a
rapid insight on the location of coarser materials and individual outliers,
while arithmetic surface roughness would be more useful to detect units or
layers that are similar on an outcrop.
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