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Procedia Computer Science 00 (2019) 000–000
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Third International Conference on Computing and Network Communications (CoCoNet’19)
Procedia Computer Science 171 (2020) 148–157
Automatic detection and characterization of Lunar Wrinkle ridges
a*
a
b
Third International
Conference
on Gandhi
Computing
and Network
Communications
Suchit Purohit
,Savita
,Nidhi
Dubeya,Prakash
Chauhan(CoCoNet’19)
Department of Computer Science,Gujarat University,Ahmedabad
Automatic detection
and characterization of Lunar Wrinkle ridges
Indian Institute of Remote Sensing , ISRO,Dehradun
a
b
Suchit Purohita*,Savita Gandhia,Nidhi Dubeya,Prakash Chauhanb
Abstract
a
Department of Computer Science,Gujarat University,Ahmedabad
b
Indian Institute of Remote Sensing , ISRO,Dehradun
Related computational advances, across various geoscience disciplines, have led to development of an upcoming field:
Computers & Geosciences. This multidisciplinary research area is attracting interest of both computer science and
planetary/space
scientists since last two decades. Under this domain, the most significant area which is gaining importance is
Abstract
application of computational intelligence to automatically extract landforms present on planetary surfaces and determine their
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of morphometric parameters like length, width, orientation and area. The software developed was
tested on DEMs (Data Elevation Model) of lunar wrinkle ridges utilizing SLDEM (Selene Lunar Data Elevation Model) data set.
The test dataset comprises of 9 lunar wrinkle ridges having 62 segments with a total length of 1414 km. The results were verified
© 2020
Thecalculations
Authors. Published
by Elsevier
by
manual
using QGIS
software.B.V.
The software was found to perform with a mean percentage deviation varying from
This to
is an
open
article
under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
3.10
5.97
for access
different
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Peer-review under responsibility of the scientific committee of the Third International Conference on Computing and Network
©
2020 The Authors.
Published by Elsevier B.V.
Communications
(CoCoNet’19)
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
© 2020 The under
Authors.
Published by
B.V. committee of the Third International Conference on Computing and Network
Peer-review
responsibility
ofElsevier
the scientific
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Communications (CoCoNet’19).
Peer-review under responsibility of the scientific committee of the Third International Conference on Computing and Network
Keywords:Lunar
wrinkle
ridge; DEM; Automatic detection; morphometric parameters evaluation;
Communications
(CoCoNet’19)
1. Introduction
Keywords:Lunar wrinkle ridge; DEM; Automatic detection; morphometric parameters evaluation;
Computational issues are becoming increasingly critical for virtually all fields of geoscience. This includes the
development of improved algorithms and models, strategies for implementing high-performance computing
1. Introduction
platforms,
or the management and visualization of the large datasets provided by an ever-growing sensor technology
Computational
issues are
becoming
increasingly
critical for virtually all fields of geoscience. This includes the
1877-0509©
2020 The Authors.
Published
by Elsevier
B.V.
development
of improved
algorithms
and license
models,
strategies for implementing high-performance computing
This is an open access
article under the
CC BY-NC-ND
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
platforms,
or the
management
visualization
ofthe
theThird
large
datasets provided
an ever-growing
sensor
technology
Peer-review under
responsibility
of theand
scientific
committee of
International
Conference by
on Computing
and Network
Communications
(CoCoNet’19)
1877-0509© 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Third International Conference on Computing and Network Communications
(CoCoNet’19)
1877-0509 © 2020 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Third International Conference on Computing and Network
Communications (CoCoNet’19).
10.1016/j.procs.2020.04.016
Suchit Purohit et al. / Procedia Computer Science 171 (2020) 148–157
Author name / Procedia Computer Science 00 (2019) 000–000
2
149
from increasing planetary missions. Such issues are applicable to scientific fields like geological modelling,
geophysics or climatology in general and planetary sciences in particular. Related computational advances, across
various geoscience disciplines, have led to development of an upcoming field: Computers & Geosciences. This
multidisciplinary research area is attracting interest of both computer science and planetary/space scientists since
last two decades. Under this domain, the most significant area which is gaining importance is application of
computational intelligence to automatically extract landforms present on planetary surfaces and determine their
morphometric attributes. Some of the key landforms present on planetary bodies are impact craters, wrinkle ridges,
faults, valleys, domes etc. Of many landforms, wrinkle ridges are the oldest one to be detected on any planetary
surface. Geologically, a ridge is a feature that consists of hills or chain of mountains that form a continuous elevated
crest for some distance. Ridges are formed by structural changes that have occurred on the lunar surface when the
basaltic lava is cooled and contracted on lunar surface [12], [11], [5] and [10].The topography of wrinkle ridge being
irregular and disconnected in nature, can be described as gently sloping from one side and drop-off from the other
side with a broad platform in between both sides [13]. In an image/DEM, a ridge can be defined as points having an
extremum (i.e. the maximum or minimum value of a function) in the direction of the largest surface curvature. Over
the years, the measurements and analysis of morphometric parameters of wrinkle ridges are done using visual
interpretation and the parameters measured using GIS-based tools with manual intervention. The method is
inefficient for large volumes of data and is subjective to individual perceptions. To overcome this problem, we
propose an algorithm for automatically detecting the ridges on lunar surface and determining their morphometric
parameters of length, width, area and orientation. The method is fully automated in the sense that whole process
from detecting it in selected region, evaluating the parameters and exporting results to an external database for
generation of reports is completely automatic and free from manual intervention.
The rest of this paper is structured as follows: Section 2 summarizes the experimental data sets. Section 3 presents
the literature survey, Section 4 presents the proposed methodology; Section 5 elaborates the results followed by
conclusion in Section 6.
2. Dataset
In this study, SLDEM data set is used. The Lunar Orbiter Laser Altimeter (LOLA) data and Selenological and
Engineering Explorer (SELENE or Kaguya, operated by the Japan Aerospace Exploration Agency) data have been
combined to generate the SLDEM digital elevation models [14]. This combination produces a near-global lunar
DEM with an effective resolution of ~60 m at the equator. The SLDEM has a typical vertical accuracy of ~3 to 4 m.
The data are given in both PDS floating-point image (IMG) and JPEG-2000 (JP2) formats. Data in the GLOBAL
subdirectory cover +/- 60 degrees in latitude and 360 degrees in longitude. Resolutions of the images of SLDEM
data are either of 128 or 256 pixels per degree. Files in the TILES subdirectory contain the 512 pixels per degree
product in the form of tiles (http://ode.rsl.wustl.edu). The developed software was tested on 9 wrinkle ridges having
a total of 62 segments and encompassing over a length of 1414 km.
3. Literature survey
The automatic detection of wrinkle ridges has always been a challenging task due to its irregular shape that
attributed to gradual erosion and degradation over a period of time. Very little work has been reported regarding
automation of detection and characterisation. An algorithm proposed in [1], is based on a combination of fractal
dimension and morphological operators for automatic detection of wrinkle ridges from Magellan Synthetic Aperture
Radar (SAR) imagery at different scales. A technique based on phase symmetry and phase congruence for automatic
ridge detection in lunar images is proposed in [6]. This approach provides better results than the plan curvature
method, but does not work for ridges having distorted shape. Wrinkle ridges typically consist of a broad arch and a
superposed sharper ridge ([8], [9]). This topography enables them to be distinguished from the surrounding terrains
by the slope change [10]. This property was exploited in the algorithm proposed in [7] . Their proposed algorithm
consists of the slope map generation; conversion to grey scale (0-255) image; phase symmetry generation of the
slope map with filter wavelength and filter scales parameters; followed by application of regional threshold to limit
the ridge candidates. The method was tested on Lunar Reconnaissance Orbiter Camera (LROC) WAC image and
topographic data from LOLA have been used in this research.
Suchit Purohit et al. / Procedia Computer Science 171 (2020) 148–157
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3
The existing approaches work towards automation of detection process only. Morphometric parameter evaluation
is manual or semi-automatic. To facilitate automatic mapping of wrinkle ridges, there is need of methodology to
automatically detect ridges as well as extract the individual segments followed by calculation of the parameters of
individual segments automatically. More or less all researchers have applied their algorithms on LOLA and/or LRO
data sets. In this study, SLDEM data set is used which is superior in terms of resolution and coverage, as compared
to LOLA data set.
4. Methodology
The flow chart of the adapted methodology is shown in Fig. 1.
Global Lunar Image
(DEM)
Small Ridge
Elimination
Orientation
Computation of each
ridge segment
Crop
Image
Contrast
Stretching
Crater Elimination
Computation of width
and length of each
ridge segment with
the help of its
topographic profile.
Pre-Processing
Noise
Reduction
Edge Detection (using
Canny edge detection
technique)
Computation of area
of all the wrinkle
ridge segments
Converting RGB
image into binary
image
Filtering Process
(by using Gaussian
filter)
Testing and
Validation against
benchmark /manual
measurements
Fig. 1. Methodology of the proposed automatic ridge detection and morphometric parameters evaluation in lunar images
4.1. Pre-processing
In the pre-processing stage, contrast stretching is applied to improve the contrast in an image by `stretching' the
range of intensity values between 0.4127 and 0.4427. As digital images are prone to various types of noises that can
be introduced during image acquisition and/or transmission process, a noise removal process was followed by
contrast stretching. For noise reduction, linear filtering method was applied using Gaussian filter with sigma value
as '0.8'.
4.2. Edge Detection and crater elimination
Edges of the enhanced image are delineated using canny edge detector with a threshold ranging from 0.25 to
0.30. The crater is the most important feature present in the planetary image which may also be represent in the
areas pertaining to ridges. These features need to be eliminated to avoid misinterpretation of ridge on boundary of
crater. In this study Circular Hough Transform (CHT) is used to segregate craters from the terrain in an image. The
Hough Transform and its several modified versions are recognized as robust techniques for curve detection. The
intent of this CHT is used to find craters; which are circular patterns within an image. In this study, crater detection
algorithm proposed in [16] below. The algorithm returns the center and radius of each crater. Accordingly area
enclosed by each crater is calculated. If the crater’s locations belong to the ridge location, then it is excluded from
elimination else removed using area filter. The image obtained after the crater elimination consists of required ridge
4
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regions, which is further analysed to retain significant ridges.
4.3. Small ridges elimination
The ridges of relatively small area may be the crater rims which may not have been handled in crater elimination
stage. In order to retain the significant ridge, the area of each ridge segment is obtained. The trial and error method
was used to choose the area threshold value and it is applied to limit the ridge area to be detected.
𝐵𝐵𝑊𝑊𝑎𝑎 >= Ar
(1)
Where BWa is a segmented image and Ar is an area threshold which limits the minimum ridge feature to be
detected. The area threshold (Ar) is image independent and thus the minimum detectable area is kept constant for all
the images. The area threshold used in this study varies from 50 to 80 and pixel connectivity is '8'. After area
filtering, the image consists of disconnected ridges. These ridges are connected using the morphological operations
of dilation and thinning. The image obtained after the Post-processing consists of significant ridge segments.
4.4. Wrinkle ridge orientation. length, width and area
The length of wrinkle ridges was calculated by dividing the dorsa (or ridge) in multiple segments on the basis of
their orientation along with the horizontal axis(x) in DEM image. The orientation of ridges is depicted through rose
diagrams. A rose diagram is defined as a circular histogram plot which displays directional data and the frequency
of each class of direction. Fig. 5 shows rose diagram for each segment of Nicol ridge. The rest of orientations are
provided in the supplementary file. A bounding box on each segment of wrinkle ridge is created on the basis of
their orientation with respect to x axis as shown in Fig. 2.The coordinates of bounding boxes (i.e. xmin, xmax, ymin,
ymax) were generated from each bounding box of segment, with the help of those two end points, the automatic
lines were generated on each segment of ridge as shown in Fig. 2. The width of ridge was calculated with the help of
topographic profile of each ridge segment. In the topographic profile of ridge segment, the distance between the two
end points gives automatic width for each segment of ridge (shown in Fig. 3). For calculating area of ridge, sum all
the area occupied by each ridge segment with the help of connected components of wrinkle ridge as shown in Fig. 4.
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Fig. 2. Calculation of length of ridge segment in an image of dorsa Nicol
Fig. 3. Calculation of rim width of segment of Nicol ridge with the help of profile
5
6
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The schematic image-based framework of the above aforementioned methodology is shown in for Dorsa Nicol is
shown in Fig. 4.
Fig. 4. Schematic framework of automatic ridge detection and morphometric parameters evaluation (1) Lunar image of Dorsum Nicol (18.0°N
and 23.0°E in Mare Serenitatis ); (2) Cropped image; (3) Pre-processing and filtering; (4) Canny edge detection; (5) Post-processing includes
crater elimination, small ridge elimination; (6) Calculating ridge length of first segment of ridge on basis of their orientation; (7) Calculating
wrinkle ridge segment width and (8) Calculating area occupied by all the segments of the wrinkle ridge.
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Fig. 5. Rose diagram showing orientation of each segment of Dorsa Nicol
7
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5. Results
The algorithms for determination of parameters were implemented in MATLAB (version R2016b) using
mapping and image processing toolboxes. The programs were executed on workstation with 12GB internal RAM
and processor Intel core i7 CPU with 3.60GHz. The program was tested on wrinkle ridges of different types using
developed algorithms. For this purpose a data set was created with 9 lunar wrinkle ridges from mare and highland
region as listed in Table 1. Fig. 6 shows the marked test sites on global Lunar map. The summary of the data set
prepared number of segments for each ridge and observed outputs of morphometric parameters are provided in the
supplementary file. For the validation of output, mean percentage deviation of each measurement was calculated
along against the manual observation using QGIS software. The program performed at different levels of accuracy
for different parameters as summarized in Table 2 and demonstrated in Fig. 7 through Fig. 10.
Fig. 6. Global image of lunar where wrinkle ridges are labeled as (a)Dorsa Nicol, (b)Dorsa Lister, (c)Dorsa Smirnov, (d)Dorsa Ewing,
(e)Dorsa Zirkel, (f)Dorsa Mawsom, (g)Dorsa Argand and (h)Dorsa Whiston
Table 1. Dataset of wrinkle ridges included in proposed algorithm
Name of ridges
Number of
segments
1
Dorsa Burnet
6
2
Dorsa Ewing
6
3
Dorsa Lister
6
4
Dorsa Smirnov
8
5
Dorsa Whiston
4
6
Dorsa Zirkel
6
7
Dorsa Nicol
11
8
Dorsa Mawson
6
9
Dorsa Argand
9
S.no.
9
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Table 2, Mean percent deviation of morphological parameters
Parameter
Mean percent deviation
(in %)
Ridge Length
5.97
Ridge Width
4.62
Ridge Orientation
3.10
Ridge Area
5.57
wrinkle ridge width
30.00
300
250
Observed ridge width (in kms)
Observed ridge length(in kms)
Wrinkle ridge length
25.00
200
y = 0.9856x
R² = 0.9876
150
100
20.00
y = 0.947x
R² = 0.9753
15.00
10.00
50
0
0
100
200
300
5.00
0.00
Expected ridge length using QGIS (in kms)
0
10
Wrinkle ridges area
Observed ridge orientation (in
kms)
0
50
100
150
Expected ridge orientation using QGIS (in kms)
Fig. 9. Observed orientation vs. manual observation
Observed ridge area (in kms)
Wrinkle ridges orientation
y = 0.9892x
R² = 0.9581
30
Fig. 8. Observed width vs. manual observation
Fig. 7. Observed length vs. manual observation
160
140
120
100
80
60
40
20
0
20
Expected ridge width using QGIS (in kms)
30000
25000
20000
y = 0.9807x
R² = 0.9962
15000
10000
5000
0
0
10000
20000
30000
Expected ridge area using QGIS (in kms)
Fig. 10. Observed area vs. manual observation
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6. Conclusions
An automated method for detection and determination of morphometric parameters of lunar wrinkle ridges has
been proposed in this research paper. Our proposed automated methodology was tested on 9 lunar wrinkle ridges
with a total of 63 segments on different regions of moon, i.e. Mare and Highland region. The proposed methodology
gives mean percentage deviation varying from 3.10 to 5.67 for different morphometric parameters. The proposed
methodology and its implementation through program have been found to give quite accurate results and thus it is
regarded as fast and robust.
Acknowledgements
This work received support from the Indian Space Research Organization under the grant ISRO/SSPO/Ch1/2016-17. We would like to thank Space applications centre, ISRO for provision of expertise, technical and
logistical support throughout the development of work depicted in this paper
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