Automated Detection of Martian Gullies from
HiRISE Imagery
Wei Li, Kaichang Di, Zongyu Yue, Yiliang Liu, and Shujuan Sun
Abstract
Gully is a type of young geological feature on the Martian sur-
face, and the study of gullies can significantly contribute to un-
derstanding of the geologic history of Martian surface. As a large
amount of high-resolution orbital images have been acquired,
manual identification and extraction of all gullies is tedious and
prohibitively time consuming. Therefore, it is desirable to devel-
op automated methods for detection of Martian gullies to support
scientific studies of the gullies. This paper presents an automated
gully detection method based on mathematical morphology
techniques. The method integrates a series of morphological
operators, including area opening and closing, Bottom-Hat trans-
formation, and path opening. Experimental results using HiRISE
images at six sites demonstrate promising performance with
detection percentage from 76 percent to 94 percent.
Introduction
Martian gullies are small, narrow, linear features incised into
steep slopes, and were first found by Mars Orbiter Camera
(
MOC
) onboard Mars Global Surveyor (
MGS
) in 2000. They usu-
ally have a dendritic alcove head and a fan-shaped apron, with
a single thread of channel linking the two parts (Malin and
Edgett, 2000). The widths of the channels range from meters to
tens of meters, and the lengths from tens of meters to hundreds
of meters; thus the channels are much smaller than the valley
networks on Mars (Carr, 2006; Gulick, 2008). Martian gullies
have been found on crater walls, terraces, and sand dunes.
They are believed to be very young in geologic time, because
craters are absent on most of their terminal deposits, and they
cut across all other features in their path such as sand dunes
(Malin and Edgett, 2000). Recent repeated observations from
High Resolution Science Experiment (
HiRISE
) images onboard
Mars Reconnaissance Orbiter (
MRO
) indicate that some gullies
are still active today (Costard
et al
., 2007; Diniega
et al.
, 2010;
Reiss
et al.
, 2010). The formation of Martian gully remains a
hot topic in planetary science community. So far, three catego-
ries of hypotheses have been proposed for Martian gully forma-
tion: dry mechanism (dry mass wasting of fine-grained materi-
als), underground wet mechanisms (release of groundwater and
liquid CO
2
), and surficial wet mechanisms (melting of shallow
ground ice and snowmelt) (Malin and Edgett, 2000; Mellon
and Phillips, 2001; Lee
et al
., 2001; Musselwhite
et al
., 2001;
Hoffman, 2002; Costard
et al
., 2002; Treiman, 2003; Head
et al
.,
2008). Further studies of gullies can significantly contribute to
the understanding of the geologic history of Martian surface.
Many high-resolution images acquired in last decades have
been utilized for studying Martian gully distribution, such
as Mars Orbiter Camera narrow angle (
MOC/NA
) images from
MGS
(Heldmann and Mellon, 2004; Heldmann
et al
., 2007),
Thermal Emission Imaging System (
THEMIS
) images from
Mars Odyssey (Bridges and Lackner, 2006), High Resolution
Stereo Camera (
HRSC
) images from Mars Express (Balme
et al.
,
2006; Kneissl
et al
., 2010),
HiRISE
, and Context Camera (
CTX
)
images from
MRO
(Gulick, 2008; Harrison
et al
., 2014). How-
ever, currently only parts of the Martian gullies have been
mapped and documented (Harrison
et al
., 2014), and most of
them have been identified manually. Since large quantities
of Martian gullies still have been unmapped and monitoring
new activities of Martian gullies is very important in Martian
geology research, it is considered being necessary to develop
automated methods for Martian gully detection.
In this paper, an automated method based on mathematical
morphology techniques is proposed for Martian gully detec-
tion. First, area opening and closing operators are used to fil-
ter the grayscale image, and then Bottom-Hat transformation
is applied to extract small dark features from the filtered im-
age; finally, non-gully features are removed by path opening
operation. Because the resolution of
HiRISE
imagery is denser
than that of any other Mars orbital images, we use
HiRISE
im-
ages to detect gullies in this research. If high-resolution digi-
tal terrain model (
DTM
) is available, an additional detection
step (relief calculation) is adopted to eliminate false positive
through relief calculation to improve the detection accuracy.
Related Work
In the past decade, many researchers tried to develop au-
tomated methods to extract features, primarily craters, on
planetary surfaces, but little attention has been paid to gully
extraction. So far, there is no publication of an automated
approach dedicated to Martian gully detection from imagery.
In previous studies, extraction method for Martian valley
network from
DTM
data has been proposed (Stepinski and Col-
lier, 2004; Molloy and Stepinski, 2007) based on the classic
run-off modeling algorithm for automated delineation of flow
paths in hydrologic modeling (Jenson and Domingue, 1988;
Tarboton
et al
., 1991; Tarboton and Ames, 2001; Statella
et al
.,
2012). Since high-resolution
DTM
s that can be used to identify
Martian gullies are rare, we use high-resolution imagery as
the primary source to detect gullies.
Wei Li is with the State Key Laboratory of Remote
Sensing Science, Institute of Remote Sensing and Digital
Earth, Chinese Academy of Sciences, P. O. Box 9718, Datun
Road, Chaoyang District, Beijing 100101, China; and College
of Tourism, Resources and Environment Science, Hunan
University of Arts and Science, 170 Dongting Road, Changde
415000, Changde Hunan, China.
Kaichang Di, Zongyu Yue, and Shujuan Sun are with the
State Key Laboratory of Remote Sensing Science, Institute
of Remote Sensing and Digital Earth, Chinese Academy of
Sciences, P. O. Box 9718, Datun Road, Chaoyang District,
Beijing 100101, China (
).
Yiliang Liu is with Space Star Aerospace Technology
Applications Co., Ltd., 82 Zhichun Road, Haidian District,
Beijing 100086, China.
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 12, December 2015, pp. 913–920.
0099-1112/15/913–920
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.12.913
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
December 2015
913
