Maritime domain awareness with commercially

Transcription

This article was downloaded by: [University of Leicester], [Nigel Bannister]
On: 14 January 2015, At: 01:34
Publisher: Taylor & Francis
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered
office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
International Journal of Remote
Sensing
Publication details, including instructions for authors and
subscription information:
http://www.tandfonline.com/loi/tres20
Maritime domain awareness with
commercially accessible electro-optical
sensors in space
a
b
N.P. Bannister & D.L. Neyland
a
Department of Physics & Astronomy, University of Leicester,
Leicester LE1 7RH, UK
b
Systems Engineering Directorate, The Charles Stark Draper
Laboratory, Inc., Cambridge, MA 02139-3563, USA
Published online: 09 Jan 2015.
Click for updates
To cite this article: N.P. Bannister & D.L. Neyland (2015) Maritime domain awareness with
commercially accessible electro-optical sensors in space, International Journal of Remote Sensing,
36:1, 211-243, DOI: 10.1080/01431161.2014.990647
To link to this article: http://dx.doi.org/10.1080/01431161.2014.990647
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the
“Content”) contained in the publications on our platform. However, Taylor & Francis,
our agents, and our licensors make no representations or warranties whatsoever as to
the accuracy, completeness, or suitability for any purpose of the Content. Any opinions
and views expressed in this publication are the opinions and views of the authors,
and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content
should not be relied upon and should be independently verified with primary sources
of information. Taylor and Francis shall not be liable for any losses, actions, claims,
proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or
howsoever caused arising directly or indirectly in connection with, in relation to or arising
out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Any
substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,
systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Downloaded by [University of Leicester], [Nigel Bannister] at 01:34 14 January 2015
Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
International Journal of Remote Sensing, 2015
Vol. 36, No. 1, 211–243, http://dx.doi.org/10.1080/01431161.2014.990647
Maritime domain awareness with commercially accessible
electro-optical sensors in space
N.P. Bannister
a
* and D.L. Neylandb
a
Department of Physics & Astronomy, University of Leicester, Leicester LE1 7RH, UK; bSystems
Engineering Directorate, The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139-3563,
USA
(Received 30 May 2014; accepted 14 October 2014)
Comprehensive maritime domain awareness includes detection, tracking, and identification of vessels, and we describe work concerned with the detection and tracking elements
of this problem. Millions of maritime vessels operate without the means for their
positions to be independently tracked, presenting a problem for safety and security of
life at sea. To date, visible wavelength imaging from space has been of limited use for
vessel detection and tracking due to poor area coverage provided by individual satellites.
This situation is now changing. We present a survey of currently operating spacecraft
carrying electro-optical imagers offering adequate imaging resolution and commercial
data availability. We model the coverage provided by 54 satellites and 85 sensors over a
target area to assess the value of these assets for space-based maritime domain awareness. The results show that useful levels of coverage can now be obtained through the
collective observations of existing spacecraft, although on-board resources will limit the
amount of imagery that can be acquired. The launch of large numbers of high-resolution
imaging nanosatellites produced by private companies will improve this coverage, and
the increasing capability of small satellite platforms offers the possibility of a dedicated
Electro-Optical Space-Based Maritime Domain Awareness constellation that can realise
the full benefit of the concept. We propose a cooperative approach, based on cloud
computing and crowdsourcing philosophies, to the operation of the ground segment and
the sharing and analysis of image data, to create an effective constellation of satellites
and associated data handling infrastructure that can be used to enhance maritime
security, and improve the safety of lives at sea.
1. Introduction
The International Maritime Organization (IMO) Safety of Life at Sea (SOLAS) convention requires automatic identification systems (AISs) to be fitted on all ships of 300 tonnes
gross or above engaged on international voyages, cargo ships in excess of 500 tonnes on
any route, and passenger ships of any size built after 2002. AIS transponders are very high
frequency systems, which transmit information including vessel identity, position, course,
and speed. The signal can be transmitted from ship to a base station on land or via a relay
using transponders on other vessels as repeaters until a shore-based station is reached.
Space-based AIS reception is possible, with exactEarth and ORBCOMM being the
two principal operators of satellite-based AIS services. Re et al. (2012) consider
approaches to assessing the system performance of a satellite-AIS constellation, while
Thomas (2011) states that eight AIS-equipped commercial satellites were currently on
orbit at the time of that publication. This number will certainly increase, with
*Corresponding author. Email: nb101@le.ac.uk
© 2015 Taylor & Francis
212
N.P. Bannister and D.L. Neyland
ORBCOMM preparing to launch 18 AIS-equipped satellites on a SpaceX Falcon-9
vehicle, and exactEarth planning to add another satellite to its current five spacecraft
constellation in 2014. However, AIS is not a perfect solution to maritime domain awareness. Carson-Jackson (2012) identifies satellite availability and service levels, data packet
conflicts in spacecraft telemetry, and the need for new data access protocols as issues
currently affecting the usefulness of the technique. Furthermore, whether the AIS receiver
is in space, on the ground, or at sea, the signal from a vessel can be turned off or
configured to generate deliberately incorrect information (Donati and Fineren 2012). And
while tens of thousands of ships are required to carry AIS, this is a small fraction of the
total number of vessels at sea; Lundquist (2010) suggests that there are in excess of
21 million recreational and fishing vessels in the USA alone, which are not required to
carry AIS. The majority are responsibly operated, but without good tracking information,
many are left vulnerable in the event of encountering difficulties offshore, while even
small boats are capable of delivering powerful weapons, drugs, or dangerous individuals.
Thus, AIS alone cannot achieve comprehensive maritime domain awareness.
Spacecraft equipped with synthetic aperture radar (SAR) can also detect vessels and
wakes, and considerable work on this topic appears in the literature (see, e.g. Suchandt,
Runge, and Steinbrecher (2010). Advantages of SAR include the wide area coverage that
can be obtained when in scanning SAR (SCANSAR) mode and insensitivity to weather.
However, amongst its current limitations are its limited use for non-metallic vessels, the
highly restricted and controlled access to data imposed by some satellite operators, and the
relatively small number of spacecraft equipped with SAR, compared to the number of
optical imaging satellites currently in service. And for power efficiency reasons, most
SAR satellites operate in a dawn–dusk orbit, which limits the times of day at which
coverage can be obtained.
It is unlikely that one approach can provide a complete solution to maritime awareness. Several studies have considered fusing data from sources including optical imagers,
SAR, and other ground and airborne systems to provide comprehensive maritime awareness (Detsis et al. 2012; Greidanus and Kourti 2006). The role of optical satellites has
been considered by authors including Bruno et al. (2010) and Ross, Arifin, and Brodsky
(2011). Several challenges are faced when attempting to obtain reliable knowledge of
maritime vessel movements by optical means. Chief among these is weather: if widespread cloud obscures a region of interest, little can be done other than to switch to
alternative monitoring approaches such as SAR.
Coverage has also been identified as a problem. In their work on counter-piracy
measures in the Gulf of Aden, Posada et al. (2011) analyse the capabilities of a combined
approach to maritime awareness using satellite AIS, SAR, and long-range identification
and tracking (LRIT, which requires vessels to report, manually, their position four times
each day). The authors note that vessels under 20 m in length were undetected because
they do not need to report AIS/LRIT data and do not appear in SAR images. Posada et al.
did not consider high-resolution imaging satellites (which could be used to detect small
vessels) for wide-area surveillance because of the limited coverage they provide. But the
number of high-resolution imaging satellites is increasing, and we show that these systems
can now make a significant contribution to maritime domain awareness, with consequent
benefits to maritime security and the safety of life at sea. The objective of the work
described in this article was to identify currently operating spacecraft and sensors that are
relevant to the problem (hereafter referred to as Electro-Optical Space-Based Maritime
Domain Awareness, EO-SBMDA) and to assess the temporal and spatial coverage of
International Journal of Remote Sensing
213
maritime environments that might be achieved through the coordinated use of these assets
when viewed as an effective ‘constellation’ of independently operated spacecraft.
2. Identifying spacecraft and sensor assets
2.1. Selection criteria
2.1.1. Imaging performance
The principal imaging performance requirement for EO-SBMDA is the ability to detect a
vessel in an image. While angular resolution is useful in predicting optical performance,
quantifying the amount of detail contained in an image is more complex. The National
Imagery Interpretability Scale (NIIRS) quantifies information content in an image by
reference to specific examples (Irvine and Fishell 1997). Originating in the intelligence
and defence community, NIIRS’s heritage is reflected in the level descriptions. For
example, Level 1 images should allow the detection of a medium-sized port facility, or
distinguishing between taxiways and runways at a large airfield, while Level 9 should
allow differentiation of cross-slot from single-slot heads on aircraft panel fasteners. Civil,
radar, infrared (IR), and multispectral (MS) NIIRS criteria have since been formulated and
are also summarized by Irvine.
NIIRS is applied to images post facto, but a NIIRS value can be predicted using the
General Image Quality Equation (GIQE; Leachtenauer et al. (1997). This regression-based
model relates image quality to fundamental instrument attributes, including ground
sample distance (GSD: the distance between adjacent pixel centres projected onto the
target), modulation transfer function (a measure of the instruments’ ability to retain
contrast as a function of the spatial frequency of the signal), and signal-to-noise ratio,
as well as post-processing effects. However, this approach presents two problems for the
current study: (1) detailed information on instrument design, optical characteristics, and
processing pipeline are required for each sensor before a GIQE-derived NIIRS rating can
be calculated; and (2) while NIIRS describes image detail in qualitative terms, a preliminary assessment of EO-SBMDA performance requires a quantitative figure of merit
that can be applied to each detection opportunity. Therefore, while NIIRS and GIQE have
important roles to play in determining and expressing image quality, they are not
implemented in this study.
An estimate of the image detail expected from an instrument can be obtained from its
instantaneous field of view (IFOV): the size of the ‘patch’ on the ground covered by a
single image pixel, below which no detail can be resolved. GSD is closely related,
expressing the distance between adjacent IFOV centres on the ground. If there are no
gaps between pixels on a sensor, then IFOV = GSD; gaps between pixels lead to IFOV <
GSD, in which case GSD is the limiting figure of merit. In many cases, we found that
GSD, IFOV, and resolution were used synonymously in the databases from which our
EO-SBMDA asset catalogue was constructed. We restrict our consideration to GSD in the
remainder of this work.
To illustrate the relationship between GSD and detection capability, a test image was
generated using an image of HMS Protector (A173) taken from an aircraft (Royal Navy
2011). The vessel has an overall length of ~89 m and a beam of ~18 m, but the image can
be scaled and resampled to approximate the detail expected for a range of vessel sizes and
imager GSD values, viewed against a realistic background. There are ~580 pixels along
the centreline from the bow to the rearmost structure of the helipad in the source image,
which we assume was taken directly over the centre of the deck. The source image
214
therefore has a scale of 89/580 = 0.15 m per pixel, corresponding to a GSD of 15 cm, 2.7
times smaller than the finest GSD in the asset list (41 cm for GeoEye-1). Figure 1 shows
simulated observations of three different sized vessels at five GSD values created from
this image.
In the case of the 15 m GSD image of the 21 m vessel, and the 6.5 m GSD image of
the 10 m vessel, the detection is effectively a single pixel (since the vessel has a diagonal
orientation with respect to the image axes). In calm seas and stable observation conditions,
where the background may be uniform over a large area, it may be possible to infer the
presence of an object based on such poorly sampled observations, but detector noise will
complicate identification and the confidence in a single pixel result will be low. However,
the wake left by a vessel can be significantly larger than the vessel itself. Optical detection
of ship wakes from space has been considered in detail by several authors (e.g. Corbane
et al. 2010), and wakes provide a useful feature from which to infer the presence of a
smaller vessel. Additionally, information from MS bands can be useful for vessel detection, as discussed briefly in Section 2.4. On the basis of these arguments, we set the upper
Figure 1. Simulated observations of three vessels of different sizes as they would appear when
imaged at five GSDs. The images have been resampled and rescaled to represent an 89 m vessel
(HMS Protector), a 21 m vessel, and a 10 m vessel as observed at 0.41, 3.0, 6.5, 15.0, and 50.0 m
GSD.
215
GSD limit for inclusion in the study to 400 m, slightly smaller than the length of the
largest vessel in operation, currently the 458.5 m Seawise Giant (Spyrou 2011).
Three caveats are highlighted in this approach. First, it neglects atmospheric effects
that will degrade image quality. Second, sea conditions, vessel shape, colour, and illumination geometry will affect an observation. And third, the arguments presented here are
qualitative, based on visual inspection of Figure 1. More detailed consideration of
environmental conditions, vessel types, and image processing algorithms would be
required to quantify the ability of a particular instrument to detect a given vessel under
specific conditions, while avoiding false alarms created by the presence of, for example,
clouds and small islands. Detailed work in this area has been conducted by, for example,
Yang et al. (2014) who discuss automated ship detection in satellite imagery using novel
approaches including sea surface analysis and consideration of vessel length to width
ratio. The type of satellite orbit can have important implications for automated recovery
techniques: for example, satellites in repeat ground track orbits (e.g. Aorpimai and Palmer
2007), which are resonant with Earth’s rotation, pass over the same location on Earth at
regular intervals, allowing the application of difference detection methods to identify
features of interest in an image. These techniques are of relevance to any future EOSBMDA system. However, the aims of the current study are to establish basic coverage
and revisit times for a set of EO-SBMDA capable space assets, and our results suggest
that a detection is possible if GSD < l/3, where l is the vessel length. This is consistent
with Corbane, Marre, and Petit (2008), who describe automated analysis of SPOT-5 HRG
panchromatic (PAN) images for detection of small ships, and find that the 5 m GSD
observations can be used to detect vessels above 14 m in length.
Finally, it is important to note that the current work is concerned with vessel detection.
The ability to identify a vessel, or distinguish between vessels in, for example, a busy
shipping lane, requires more detail. From Figure 1, l/GSD ~ 14 appears to provide
sufficient information to identify variations in shading in different parts of the vessel
(6.5 m GSD in the case of the 89 m vessel), while l/GSD > 30 is required to distinguish
between specific structures on board. Although such performance levels are very demanding to meet, particularly for small vessels, new technologies in imager design offer
answers to this challenge. For example, by adopting two-dimensional imaging sensors
with high frame rate capability in place of the more traditional line-scanning sensors
commonly found in earlier imager designs, the imagers and associated ‘super resolution’
processing methods used by Skybox Imaging (2014) combine multiple images of a target
into a single, higher-resolution frame that can result in very-high-resolution data products,
improving the probability of vessel detection and identification. See Park, Park, and Kang
(2003) for an overview of this technique.
2.1.2. Data availability
A further requirement for inclusion in the EO-SBMDA constellation is commercial data
availability. An asset does not need to be commercially owned and operated, as long as
the data it generates can, in principle, be accessed ‘for fee or free’. Hence, the assets
identified in this work include a mix of commercial and agency-owned spacecraft.
Resources, including listings in the Committee on Earth Observation Satellites (CEOS)
Handbook (see Section 2.2.2) and vendors of satellite imagery, including MapMart
(2014), and websites of satellite operators and agencies, are used to determine whether
access to data is tightly constrained or open.
216
While data may be commercially accessible, there will, in most cases, be a lower limit
on the length of time between acquisition of an image by the spacecraft and the availability of the image to the EO-SBMDA system. This data latency is typically dominated
by the period of time that elapses between the image being acquired and the next
opportunity to downlink the image to the ground. The use of satellite relays, such as
the European Data Relay System (Agnew, Renouard, and Hegyi 2012) currently under
development, will relax this restriction for those satellites fitted with compatible communications systems, such as the Sentinel-2 spacecraft, which will form part of ESA’s
Copernicus Earth Observation Programme (see Section 7).
Political factors can also introduce significant latency into the system. For example,
US Presidential Decision Directive 23 introduced the principle of ‘shutter control’ in
1994. Shutter control allows government agencies to restrict the areas of Earth where
commercial systems can acquire data, or impose a delay in the release of data, when
national security concerns are deemed to require it (Jakhu 2010). Other nations reserve
similar rights, and although the US shutter control restriction has never been implemented
in practice, such measures remain an issue for commercial EO satellite operators.
2.1.3. Operational status
Only spacecraft that were in current operation at the time of the study are included.
Satellites whose missions have ended are not considered nor are those still in commissioning or due for imminent launch.
2.2. Search methodology and sources
A considerable amount of information exists on Earth-orbiting satellites currently in
operation; these data are contained in a variety of publications and online catalogues.
Several information sources have been used in this study to achieve as comprehensive and
accurate an asset list as possible. The search and inclusion procedures for this preliminary
phase are described in the following.
2.2.1. OSCAR
The Observing Systems Capability Analysis and Review Tool (OSCAR) (World
Meteorological Organization 2014) is an online database of Earth Observation satellites
and instrumentation, produced and maintained by the World Meteorological Organization.
The system is restricted to spacecraft with an Earth Observation agenda but includes
engineering/technology demonstration satellites carrying Earth Observation instrumentation. OSCAR’s catalogue includes instruments providing spatial resolutions from less than
1 m up to a few tens of metres and was used to generate the initial EO-SBMDA asset list.
Most of the data on GSD and field of view parameters incorporated into the EO-SBMDA
model are taken from this resource.
2.2.2. CEOS earth observation handbook
The CEOS coordinates civil space-borne observations of the Earth and includes space
agencies and national/international organisations as its members. The CEOS, in collaboration with the European Space Agency (ESA), produces a handbook and an accompanying
online database (Committee on Earth Observation Satellites 2014) of EO assets, which
217
includes information on the availability of data from specific instruments. Where access
information is available for an instrument, the access level is indicated by a descriptor set
to Open Access, Constrained, or Very Constrained. For the purposes of this preliminary
study, instruments offering Open Access or Constrained data access are included.
Instruments with Very Constrained access are eliminated unless evidence can be found
from other sources that data can be obtained publicly for free or fee.
2.2.3. eoPortal
The origins of eoPortal (2014) are the Information on Earth Observation system, INFEO,
a European Commission programme to develop a suite of information access services
tailored for the Earth Observation community (Mills, Kjeldsen, and Shipp 1998). The
resource includes a directory of satellites and provides more detailed information than
typically available in OSCAR or the CEOS Handbook on the design of spacecraft and
instrumentation, along with details of project history, spacecraft operations, and data
products. The eoPortal entry for a mission was used where more detailed information
on sensor orientation or operating characteristics was required.
2.3. The SBMDA asset list
There are currently ~1170 active satellites orbiting the Earth (Union of Concerned
Scientists 2014). From this list, 165 spacecraft were identified as having an Earth science,
Earth observation, or engineering/technology demonstration role that included imaging
payloads, excluding those explicitly listed as military satellites. The payload of each of
these satellites was examined for high-resolution imaging instrumentation generating
accessible data; at this stage, 111 spacecraft were rejected for reasons including the
absence of imaging systems (e.g. the four COSMO-SkyMed satellites are Earth Science
satellites but carry SAR instruments only), highly restricted data availability (e.g.
KOMPSAT-3 and the YAOGAN series of satellites), or inadequate optical resolution
(such as IMS-1 and GOES-15). Table 1 summarizes the 54 spacecraft that remained
after this selection process and that are therefore potentially relevant to the commercial
SBMDA concept.
2.4. EO sensors
Table 2 summarizes the 85 high-resolution optical imagers carried by the spacecraft
identified in Table 1. The grey section lists, with justification, notable exclusions from
the model. All sensors operate in the visible range of the spectrum, often with several
bandpasses defined. The resolution of an instrument is dependent on wavelength, and
Table 2 follows the convention adopted in OSCAR by distinguishing between resolution
in the PAN and MS channels where defined. If several modes with differing optical
performance characteristics are available in an instrument, the ‘best’ (smallest) GSD is
adopted. Figure 2 shows a histogram of sensor GSD illustrating that a significant fraction
of the sensors offer performance at the higher-resolution end of the GSD range. Detailed
information on the wavelength-dependent resolution of instruments is available in the
works cited in this article. In addition, many instruments include IR channels, and it has
been shown (e.g. Wu et al. 2009) that the contrast between ships and the ocean surface in
daytime imaging is better at near-IR wavelengths, offering the possibility of enhanced
detection probability. IR techniques may also have a role in night time monitoring, with
Satellite name
SPOT 4
LANDSAT 7
IKONOS 2
EOS-TERRA
EO-1
EROS A1
QUICKBIRD 2
PROBA-1
SPOT 5
EOS-AQUA
IRS-P6
FORMOSAT-2
IRS-P5
BEIJING 1
TOPSAT
EROS B
CALIPSO
RESURS-DK 1
ARIRANG 2
CARTOSAT-2
SAUDISAT-3
WORLDVIEW-1
CARTOSAT-2A
IMS-1
FENGYUN-3A
RAPIDEYE 2
RAPIDEYE 5
RAPIDEYE 1
RAPIDEYE 3
RAPIDEYE 4
HUANJING 1A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1998-017A
1999-020A
1999-051A
1999-068A
2000-075A
2000-079A
2001-047A
2001-049B
2002-021A
2002-022A
2003-046A
2004-018A
2005-017A
2005-043A
2005-043B
2006-014A
2006-016B
2006-021A
2006-031A
2007-001B
2007-012B
2007-041A
2008-021A
2008-021D
2008-026A
2008-040A
2008-040B
2008-040C
2008-040D
2008-040E
2008-041A
Designation
FR
US
US
US
US
ISRA
US
ESA
FR
US
IND
PRC
IND
PRC
UK
ISRA
US
CIS
SKOR
IND
SAUD
US
IND
IND
PRC
GER
GER
GER
GER
GER
PRC
Country
824
703
680
703
691
542
441
654
826
704
823
892
619
702
704
517
703
570
698
637
677
493
644
637
835
638
643
647
639
639
665
Apogee (km)
Satellites in the model listed in order of launch date, with basic orbital parameters.
No.
Table 1.
821
702
678
701
676
525
439
542
824
701
817
890
618
681
681
506
702
565
675
634
655
491
622
617
820
621
616
613
620
620
627
Perigee (km)
101.3
98.8
98.3
98.8
98.4
95.3
93.4
96.7
101.4
98.8
101.3
102.8
97.1
98.6
98.6
94.8
98.8
96.0
98.5
97.4
98.1
94.5
97.4
97.3
101.5
97.3
97.3
97.3
97.3
97.3
97.6
Period (min)
98.5
98.2
98.1
98.2
98.0
97.6
97.2
97.5
98.6
98.2
98.8
98.9
97.9
97.9
97.9
97.4
98.2
69.9
98.2
98.0
97.8
97.3
97.9
97.8
98.6
97.9
97.9
97.9
97.9
97.9
97.8
Incl. (°)
218
HUANJING 1B
GEOEYE 1
THEOS
DEIMOS-1
DUBAISAT 1
UK-DMC 2
METEOR-M 1
OCEANSAT 2
WORLDVIEW-2
ALSAT 2A
FENGYUN-3B
RESOURCESAT-2
X-SAT
SAC-D
NIGERIASAT-2
NIGERIASAT-X
RASAT
PLEIADES 1A
ARIRANG 3
SPOT 6
PLEIADES 1B
GKTRK-2
LANDSAT 8
2008-041B
2008-042A
2008-049A
2009-041A
2009-041B
2009-041C
2009-049A
2009-051A
2009-055A
2010-035D
2010-059A
2011-015A
2011-015C
2011-024A
2011-044B
2011-044C
2011-044D
2011-076F
2012-025B
2012-047A
2012-068A
2012-073A
2013-008A
PRC
US
THAI
SPN
UAE
UK
CIS
IND
US
ALG
PRC
IND
STCT
ARGN
NIG
NIG
TURK
FR
SKOR
FR
FR
TURK
USA
671
685
826
663
678
662
820
724
767
673
829
822
822
655
705
699
697
699
696
699
699
689
704
621
673
825
660
663
660
818
722
766
671
826
818
802
653
690
672
666
697
679
697
697
669
702
97.6
98.3
101.4
98.0
98.2
98.0
101.3
99.2
100.2
98.2
101.5
101.3
101.1
97.8
98.7
98.5
98.4
98.7
98.5
98.7
98.7
98.7
98.8
97.8
98.1
98.8
98.0
98.0
98.0
98.6
98.2
98.5
98.1
98.8
98.7
98.7
98.0
98.2
98.2
98.2
98.2
98.2
98.2
98.2
90.2
98.2
Notes: Note that the final column provides the orbital inclination of the spacecraft. No Two-Line Element set was identified for Pleiades 1B, so we adopt orbit specification inferred
from eoPortal, based on modified Pleiades-1A orbit. ALG, Algeria; ARGN, Argentina; CIS, Commonwealth of Independent States [of former Soviet Republics]; ESA, European Space
Agency; FR, France; GER, Germany; IND, India; ISRA, Israel; NIG, Nigeria; PRC, People’s Republic of China; SAUD, Saudi Arabia; SKOR, South Korea; SPN, Spain;
STCT, Singapore/Taiwan; THAI, Thailand; TURK, Turkey; UAE, United Arab Emirates; UK, United Kingdom; USA, United States of America.
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
219
220
Table 2.
results.
High-resolution optical instruments currently in operation, based on OSCAR search
Acronym
Mission(s)
GSD (m)
ALI
ASTER
EO-1
EOS-Terra
10 (PAN), 30 (MS)
15–90 (channeldependent)
AWiFS
BGIS-2000
CHRIS
ResourceSat-1 & -2
QuickBird-2
PROBA-1
CMT
DMAC
EOS-C
ETM+
Geoton-1
GIS
HiRI
HRG
HRS
Beijing-1
DubaiSat-1
GšktŸrk-2
LandSat-7
Resurs-DK
GeoEye-1
Pleiades-1A & 1B
SPOT-5, SaudiSat 3
SPOT-5
HRVIR
HSC
HSI
Hyperion
IRIS (X-Sat)
IRMSS
KMSS
SPOT-4
SAC-D
Huan Jing-1A
EO-1
X-Sat
Huan Jing-1B
Meteor-M N1
LISS-3
MISR
MODIS
MRI
MS
ResourceSat-1
ResourceSat-2
ResourceSat-1
ResourceSat-2
Feng Yun 3A Feng
Yun 3B
EOS-Terra
EOS-Aqua
NigeriaSat-2
THEOS
56
0.6 (PAN), 2.4 (MS)
18–36 (reduced/full
spectral res.)
4
2.5 (PAN), 5 (MS)
2.5 (PAN), 10–20 (MS)
15 (PAN), 30 (MS)
1 (PAN), 2–3 (MS)
0.41 (PAN), 1.64 (MS)
0.7 (PAN), 2.8 (MS)
5 (PAN), 10–20 (MS)
10 cross-track, 5 alongtrack
20 (MS), 10 (PAN)
250–300
100
30
12
150–300
60–120 (wavelengthdependent)
23.5
MSC
Mx-T
NAOMI
NAOMI
NIRST
OCM
OIS
OLI
OSA
PAN
KOMPSAT-2
IMS-1
AlSat-2
SPOT-6
SAC-D
OceanSat-2
RASAT
LandSat-8
IKONOS
CartoSat-1
LISS-4
MERSI-1
Notes
OSCAR reports ASTER
short-wave channels
ceased functioning in
2008
Error in OSCAR (quotes
resolution of 23.5 km)
5.8
250
250
250
32
15
1 (PAN), 4 (MS)
36
2.5 (PAN), 10 (MS)
2 (PAN), 8 (MS)
350
360 × 236
7.5 (PAN), 15 (MS)
15 (PAN), 30 (MS)
0.8 (PAN), 3.3 (MS)
2.5
Constrained access but
some evidence of
commercial intent
Constrained access but
some evidence of
commercial intent.
OSCAR 2.5 km
resolution incorrect
(Continued )
Table 2.
(Continued ).
Acronym
Mission(s)
GSD (m)
PAN
CartoSat-2, -2A & -2B 1
PAN
PIC
PIC-2
RALCam-1
THEOS
EROS-A
EROS-B
REIS
RSI
SLIM6
RapidEye 1 to 5
FORMOSAT-2
Beijing-1, Deimos-1,
NigeriaSat-X, UKDMC-2
LandSat-8
NigeriaSat-2
CALIPSO
WorldView-2
WorldView-1
HuanJing-1A & -1B
TIRS
VHRI
WFC
WV110
WV60
WVC
221
2
1.9
0.7
2.8 (PAN), 5.6 (MS)
Notes
Very constrained access
but some evidence of
commercial intent
CEOS constrained access
Imaging operations
suspended since 17
August 2010 due to low
demand (OSCAR)
6.5
2 (PAN), 8 (MS)
32 (Beijing-1) 22 (others)
120
2.5 (PAN), 5 (MS)
125
0.46 (PAN), 1.84 (MS)
0.5
30
Notes: The mission(s) on which each instrument flies is/are indicated. Where spatial resolution depends on the
imaging channel used, PAN indicates the panchromatic channel, MS indicates the multispectral channels.
Figure 2.
Distribution of sensor GSD performance in the constellation.
clear benefits to the EO-SBMDA concept. However, the ability to detect vessels at night
in the IR is a complex issue beyond the scope of the current article, and we restrict our
consideration of IR operation to the relaxation of the daylight-only condition for observations in some results presented in Section 5.3.
222
Figure 3. Sensor GSD versus cross-track swath width, showing the general trend of smaller fields
at higher resolution.
A consequence of the requirement for high spatial resolution imagery is that the fields
of view of the selected sensors are relatively small. Figure 3 shows the relationship
between GSD and cross-track swath width (in kilometres on the surface of the Earth)
for sensors in the model, illustrating the general trend for higher-resolution systems to
have smaller fields of view due to the design demands placed on optics and focal plane
sensors. A selection of sensors are identified in Figure 3, including the highest-resolution
sensor, GIS on GeoEye (0.41 m GSD, 14.4 km cross-track width), and the sensor with
widest swath width (MERSI on FengYun 3A, 250 m GSD, ~ 2900 km cross-track width).
Thus, a trade-off exists between resolution and footprint. While high spatial resolution is
required to detect a vessel, large fields of view are required to maximise the probability of
a vessel being captured at a given time and location. For practical EO-SBMDA, a
constellation of high-resolution sensors is therefore required to generate a usefully large
total footprint.
3. The STK EO-SBMDA model
3.1. Satellite inclusion
The EO-SBMDA model was constructed in Systems Tool Kit (STK) v.10, produced by
Analytical Graphics Inc. STK includes a database of orbital parameters for Earth orbiting
spacecraft, updated on a regular basis. For many spacecraft with EO sensors, it includes
information on field of view, field of regard (FOR), and pointing direction. In this study,
fields of regard (the range of angles within which an instrument can be pointed) were
ignored, and each imager was assumed to have a pointing direction fixed in the centre of
that FOR. This reflects an underlying assumption in the study, which is that the mode of
operation is passive – no targeted observations are assumed, and sensors are pointed
towards nadir. Note, however, that while assuming nadir-only operation is convenient in
simplifying the model and the concept of operations, in practice it is possible that agile
223
satellites with imagers of sufficiently high resolution could be operated to point the sensor
towards the horizon, exploiting the curvature of the Earth to enhance the area coverage
rate while still meeting the resolution criteria.
Where available, the STK FOV definitions were used in the model after crosschecking dimensions and orientation with data in OSCAR and eoPortal. GSD performance data were incorporated in postprocessing of STK outputs using codes written
in IDL.
3.2. Maritime area
The fields of view of some of the highest-resolution imagers are narrow (tens of kilometres). Thus, it was necessary to model satellite coverage on the Earth’s surface using
relatively fine-resolution logging, and this was achieved by dividing the surface of the
Earth into cells of area 3 km × 3 km. At any instant in time, an entire cell was regarded as
observed if an imager field of view contained any part of that cell. Modelling large
numbers of instrument fields on this scale for the entire planet places significant demands
on computational power, and instead, a ‘test area’ of ocean was defined within which to
characterise performance. This region was defined with the assistance of the New Zealand
Defence Technology Agency and is located inside the New Zealand Exclusive Economic
Zone (NZEEZ), bounded by the coordinates [27° S, 169° E], [27° S, 179° E], [35° S, 169°
E], [35° S, 179° E], which has dimensions of approximately 926 × 926 km.
Due to the high-inclination orbits of all but one of the spacecraft in the constellation,
the ground tracks converge towards higher northern and southern latitudes and are at their
most widely spaced (on the dayside) at a latitude of approximately 8° S. Hence, the
coverage results presented in this study are representative of those that would be obtained
for points within latitudes 27° S and 35° S and between 19° N and 27° N. Zones poleward
of these regions will enjoy progressively improved coverage, while points on a line of
latitude ~8° S will see the lowest coverage. However, the reduction is expected to vary
with the cosine of latitude, and so even along this parallel, the density of coverage is
expected to be ~90% of the level determined in the test zone.
3.3. Maritime vessels
The primary objective of the study was to understand the nature of coverage as a function
of position within the test area. However, to estimate performance in tracking specific
targets, nine maritime vessels were included in the STK scenario. Vessels were identified
using records published by Ports of Auckland Limited (Ports of Auckland Limited 2014),
and their tracks were obtained from AIS histories available online (Sailwx.info 2014). The
tracks were time-shifted so that all nine vessels appeared inside the test area within the
same 2-week period covered by the scenario, though their tracks were unchanged. The
vessels were British Security, Celebrity Solstice, Laust Maersk, Lica Maersk, Morning
Miracle, Ocean Village, Oosterdam, Sea Princess, and Sun Princess. A 10th vessel was
included on a manually constructed track leaving Opua NZ, bound for Newcastle,
Australia. The significance of this vessel is discussed in Section 5.3.2.
4. Constellation properties
Table 1 shows that all of the spacecraft are in low earth orbit (LEO, 400–900 km altitude).
This is a selection effect imposed by the imaging resolution requirements described
224
earlier. Such performance can be achieved from higher orbits using large, diffractionlimited optics. For example, the 2.4 m-diameter Hubble Space Telescope mirror has an
angular resolution of ≈0.50 arcseconds, which, neglecting the effects of the Earth’s
atmosphere, is sufficient to resolve features of order 10 m from Geostationary Earth
Orbit (GEO). However, such large optics are typically found only in large observatoryclass science missions and military surveillance satellites (Norris 2011), while the majority
of satellites tasked with commercial and scientific Earth observation roles are considerably
smaller. To resolve a 10 m feature from LEO (e.g. 700 km altitude) requires an angular
resolution of ~3 arcseconds – achievable at visible wavelengths using a mirror with a
diameter of a few centimetres. This is one reason for the preferential use of LEO for highspatial-resolution-imaging spacecraft. Such orbits also allow whole-Earth imaging over a
period of a few days, at the cost of image cadence (revisit time). In contrast, imaging
spacecraft in GEO observe the same region of the Earth continuously and must be
repositioned if the target is not within the FOR.
Figure 4 shows the spacecraft ground tracks; all but one of the satellites are in Sunsynchronous orbits (the exception being RESURS-DK-1), and the clustering of tracks
illustrates that the majority of spacecraft are found in orbits with similar ascending node
(Ω) or equivalently, equator crossing times – the local time at which the spacecraft crosses
the Earth’s equator from south to north. Crossings are typically selected for mid-morning
to ensure optimum lighting conditions. Crossing times around local noon are rarely used,
since the lack of shadows cast by objects makes the task of discerning features and
establishing the height of an object more difficult, and also because the amount of
convection cloud increases as the day progresses, leading to poorer viewing conditions.
The histogram in Figure 5 is based on a simulation covering approximately 2 weeks of
Figure 4. Ground tracks of the satellites, reflecting the distribution at 18:00:00 UTC on 1 August
2013. The symbols represent the instantaneous location of each satellite, and the ground track for the
following 97 min is shown (approximating the typical orbital period as given in Table 1). Areas of
the globe in darkness are shown by the shaded region on the right side of the map. The plot
illustrates the significant clustering of assets, found around similar mid-morning and mid-evening
local times. Satellite names removed to improve clarity.
225
Figure 5. Frequency distribution of spacecraft local solar times evaluated over a 2-week period
from 1 to 16 March 2013, at 5-min resolution, for all spacecraft in the draft asset catalogue. The bars
show the percentage of total mission elapsed time (summed over all the spacecraft) spent at a given
LST and illustrate the clustering of assets around 10:30–11:00 and 22:30–23:00.
mission elapsed time from 1 to 16 March 2013. The local solar time of the sub-satellite
point was recorded at 5-min intervals, and the results show that the most common local
solar time for an observation on the day-lit side of the planet is 10:30 and (consequently)
22:30 on the night side. The bias towards morning observations is understood to be due to
the increasing convection present in the atmosphere over land later in the day when the
ground has had time to warm under the Sun; this increases atmospheric turbulence and
degrades image quality.
5. Preliminary results
5.1. 9 Day all-sensor coverage estimation
The first model run covered 9 days of mission elapsed time from 00:00:00 UT on 1
August – 23:59:59 UT on 9 August 2013. All sensors in Table 2 were considered, and
only observations made while the Sun was above the horizon for a given point in the test
area were considered viable. Figure 6 shows results from this calculation, which illustrate
that all points in the test area are observed on between ~90 and 140 separate occasions,
with total exposure times ranging between ~800 and ~1350 s.
5.2. One-day coverage estimation
Two further model runs were conducted, reflecting coverage obtained on 3 August 2013.
In the first instance (all sensors included), total exposure times and number of accesses
were found to be approximately 11% of the values obtained in the 9-day model, as
expected given the relatively short period orbits of the EO-SBMDA satellites. Plots of the
all-sensor 1-day coverage results look similar to re-scaled versions of the 9-day maps in
Figure 6 and are not reproduced here.
226
Figure 6. Results from the 9-day coverage calculation. (a) Total coverage (the amount of time
spent viewing a given spot within the shaded test area) in seconds over the 9-day period; the
minimum, average, and maximum times are 860, 1036, and 1213 s, respectively. (b) The total
number of accesses (separate observations) within the time period. All locations are accessed: the
minimum number of accesses is 99, the mean is 119, and the maximum is 138. The region north of
Auckland is visible as the grey outline at the bottom of these plots, and values on the horizontal/
vertical axes represent longitude and latitude, respectively. Note that a + symbol in the legend
denotes ‘in excess of the maximum quantified value’.
In the second 1-day model, only sensors with GSD ≤ 30 m were considered; the total
observation time and number of accesses for points in the test area are shown in Figure 7.
Figure 8 summarizes the gaps between observations and the consequent response time. In
calculating the time elapsed between the end of one observation and the start of the next,
the maximum gap always corresponds to night-time when we assume no observations
take place. This gap is found to be in the range ~14.7 to ~19.5 hours, consistent with the
spread in equator crossing times for the main group of satellites as shown in Figure 4.
This obvious result illustrates the importance of extending EO-SBMDA operations into IR
Figure 7. Results from the 1-day coverage calculation including sensors with GSD < 30 m. (a)
Total coverage time (seconds) spent viewing a given spot within the test area over the 24-hour
period; all areas are covered, with minimum, mean, and maximum observation times of 3, 31, and
70 s, respectively. (b) The total number of accesses (separate observations) within the time period.
Hundred percent of the area is observed at this GSD level, with the number of accesses ranging
between 1 and 10.
227
Figure 8. (a) The maximum inter-observation gap (in seconds) during a single period of daylight,
for sensors with GSD ≤ 30 m. Note that the minimum gap (0 s) indicates that only a single
observation was made and therefore the gap is effectively undefined. The mean and maximum gap
durations are 3104 and 37680 s, respectively. (b) The average response time for sensors with
GSD ≤ 30 m (in seconds) in a single period of daylight. This figure expresses the average time
between an observation being requested and the next opportunity to image a particular location in
the test area. The minimum, mean, and maximum average response times are 4145, 7883, and
37,680 s respectively. Note that 37,680 s corresponds to the total daylight period in the simulation,
and hence the 0.02% of the test zone, which is unobserved in this period, shows this value for both
gap length and response time.
for night-time vessel tracking if possible. However, it is also useful to consider the
maximum inter-observation gap within a single daylight phase of operation, and this is
shown in Figure 8 (left), indicating gaps of between ~0.5 and ~2.7 hours (note that a gap
of 0 s indicates that only a single observation was made in that region, and hence, the gap
period is undefined). Also shown is the response time of the system, which is the timeaveraged gap between observations in the 1-day period (considering daylight hours only),
divided by 2. This parameter can be interpreted as the average time elapsed between the
observation of a point being requested and the observation being made. When all sensors
are included, the average response time is ~1 hour, and the maximum response time is ~3
hours. Restricting consideration to sensors with GSD ≤ 30 m increases the average
response time to ~2.2 hours (Figure 8, right) and the maximum response time to ~6 hours.
5.3. Vessel contacts
5.3.1. General results
The tracks of the nine named vessels while inside the test area are shown in Figure 9, with
black dots indicating points where the vessel was in the field of view of a sensor during
daylight hours. Table 3 provides more detailed information on the number of fixes
obtained for each vessel, along with the average distance travelled, and the average
time, between fixes. Although a detailed consideration of night-time IR observations is
outside the scope of this article, the results obtained when the daylight requirement is
relaxed are shown in parentheses.
228
Figure 9. Tracks of the nine vessels modelled using historical AIS data within the entire extent of
the test area, presented in cylindrical equal area projection. The vessels depicted are (a) British
Security; (b) Celebrity Solstice; (c) Laust Maersk; (d) Lica Maersk; (e) Morning Miracle; (f) Ocean
Village; (g) Oosterdam; (h) Sea Princess; (i) Sun Princess. Black circles indicate points where the
vessel was within the field of view of a sensor, during daylight hours. Note that the single contact
for Morning Miracle occurred on the boundary of the test area and is not visible on the plot. Refer to
Table 3 for details of the spatial and temporal distribution of fixes in each case.
5.3.2. Ship 10
Ship 10 was an attempt to consider a case similar to that of the Niña, a 21 m-long US
schooner which left Opua on the North Island of New Zealand on 29 May 2013 bound for
Newcastle, Australia – a journey of approximately 2400 km. The estimated duration of the
voyage was 10 days, but she was reported overdue on 12 June 2013; last contact with the
vessel was on 4 June when she was approximately 600 km WNW of Cape Regina, in 8 m
seas. The official search ended in early July 2013 without success, after overflying an area
of 1.9 million square kilometres. The hunt then continued with a crowdsourcing campaign
in which satellite imagery provided by DigitalGlobe (2013) and posted on the TomNod
website was searched by volunteers for signs of the vessel or her crew. This approach has
been used in several recent ‘crisis mapping’ campaigns and is likely to see increasing use
in future (Meier 2012). But in the case of Niña, although a feature resembling an inflatable
lifeboat was identified in an image acquired on 3 August 2013, the extended search was
ultimately unsuccessful. Niña carried only manually activated emergency beacons, and all
seven lives on board are now presumed lost (Flannery 2013).
British Security 891.5 km
110.0 hours
Morning Miracle
115.8 km 6.7 hours
Celebrity Solstice
538.9 km 28.2 hours
Ocean Village 1645.3 km
141.8 hours
9
2
2
1
1
71
24
21
13
7
12
6
4
2
1
1
1
1
1
1
50
14
9
4
1
400
200
50
25
7
400
200
50
25
7
400
200
50
25
7
400
200
50
25
7
400
200
50
25
7
Sun Princess 525.9 km
17.3 hours
9
2
2
1
1
152
55
46
30
11
25
8
6
3
1
9
2
1
1
1
98
32
21
11
2
All
Number of contacts
Day
GSD
137.8
286.0
286.0
0.0
0.0
173.0
273.8
273.8
271.6
283.0
565.4
5.5
8.9
1.5
0.0
0.0
0.0
0.0
0.0
0.0
199.3
65.0
67.8
77.6
0.0
Day
137.8
286.0
286.0
0.0
0.0
173.0
366.2
366.2
366.2
631.3
205.6
232.2
232.2
238.2
0.0
22.7
27.2
0.0
0.0
0.0
187.8
278.9
278.9
290.9
72.1
All
Max gap (km)
58.4
263.0
263.0
525.9
525.9
23.2
68.6
78.3
126.6
235.0
44.9
89.8
134.7
269.4
538.9
115.8
115.8
115.8
115.8
115.8
17.8
63.7
99.1
222.9
891.5
Day
58.4
263.0
263.0
525.9
525.9
10.8
29.9
35.8
54.8
149.6
21.6
67.4
89.8
179.6
538.9
12.9
57.9
115.8
115.8
115.8
9.1
27.9
42.5
81.0
445.8
All
Distance between
contacts (km)
1.9
8.7
8.7
17.3
17.3
2.0
5.9
6.8
10.9
20.3
2.4
4.7
7.1
14.1
28.2
6.7
6.7
6.7
6.7
6.7
2.2
7.9
12.2
27.5
110.0
Day
(Continued )
1.9
8.7
8.7
17.3
17.3
0.9
2.6
3.1
4.7
12.9
1.1
3.5
4.7
9.4
28.2
0.7
3.4
6.7
6.7
6.7
1.1
3.4
5.2
10.0
55.0
All
Time between
contacts (hours)
Summary of results for tracking of the nine vessels included in the model using historical AIS data, over 9 days, as a function of sensor GSD.
Distance and time in zone
Vessel
Table 3.
229
33
11
9
6
2
9
2
1
1
1
26
9
7
4
2
14
6
6
4
2
532.8
44.6
44.6
0.0
0.0
138.8
0.0
0.0
0.0
0.0
50.5
64.7
102.9
1.0
0.0
153.0
0.0
0.0
0.0
0.0
Day
273.1
387.9
502.9
516.5
873.6
138.8
70.3
0.0
0.0
0.0
92.0
92.0
102.9
262.6
264.3
153.0
75.7
75.7
44.3
72.2
All
Max gap (km)
126.5
696.0
696.0
1392.0
1392.0
156.2
468.7
468.7
468.7
468.7
29.5
83.5
125.3
250.6
501.2
132.3
529.1
529.1
529.1
529.1
Day
42.2
126.5
154.7
232.0
696.0
52.1
234.3
468.7
468.7
468.7
19.3
55.7
71.6
125.3
250.6
37.8
88.2
88.2
132.3
264.6
All
Distance between
contacts (km)
3.9
21.5
21.5
43.0
43.0
5.2
15.6
15.6
15.6
15.6
1.4
4.0
5.9
11.9
23.7
4.1
16.3
16.3
16.3
16.3
Day
1.3
3.9
4.8
7.2
21.5
1.7
7.8
15.6
15.6
15.6
0.9
2.6
3.4
5.9
11.9
1.2
2.7
2.7
4.1
8.1
All
Time between
contacts (hours)
Notes: For each vessel and GSD, the total number of contacts is listed, along with the maximum gap between contacts, and the average separation between contacts in km and hours.
‘Day’ columns indicate performance based on daytime-only operation, while columns headed ‘All’ give values assuming that night time IR operation is included. The total distance
travelled and the time spent within the test zone are given under the vessel name.
Laust Maersk 529.1 km
16.3 hours
Oosterdam 501.2 km
23.7 hours
Lica Maersk 468.7 km
15.6 hours
11
2
2
1
1
3
1
1
1
1
17
6
4
2
1
4
1
1
1
1
400
200
50
25
7
400
200
50
25
7
400
200
50
25
7
400
200
50
25
7
All
Number of contacts
Sea Princess 1392.0 km
43.0 hours
GSD
Day
(Continued ).
Distance and time in zone
Vessel
Table 3.
230
231
Although the last reported position of the Niña is outside the test area, the question of
how often a vessel of this size might have been observed with the EO-SBMDA constellation is relevant, particularly given the very large search area that had to be covered
because of uncertainty in the last location of the vessel. Insufficient detail is known about
the movements of Niña on her final voyage to reproduce her track, and so Ship 10 was
simply configured to leave Opua for Newcastle at 12:00 UTC on August 1st 2013,
rounding the north tip of the North Island and heading due west towards Newcastle at 4
knots, the last reported speed of the vessel.
The results of the analysis are shown in Figure 10. Each panel shows the track of the
vessel within the test area; filled circles indicate points where the vessel would have been
in the field of view of a sensor with GSD performance at or better than the level indicated.
These plots illustrate the number of contacts that could be made (assuming clear sky
conditions, during daylight hours) with vessels covering a range of sizes, and span 4 days
of observation. The maximum distance between contacts is summarised in the top right
Figure 10. The track of Ship 10 inside the test area, presented in cylindrical equal area projection.
The panels represent observations made at GSD values better than the indicated value ((a) GSD
equal to or better than 400 m (GSD ≤ 400 m); (b) GSD ≤ 200 m; (c) GSD ≤ 100 m; (d) GSD ≤ 50 m;
(e) GSD ≤ 25 m; (f) GSD ≤ 7 m). Circles represent daytime observations of the vessel along its track
using sensors offering GSD in the indicated range. The maximum distance between observations
(contacts) is indicated assuming daytime-only operations and also allowing for night-time IR
observations (value in parentheses). In the specific case of the Niña (a 21 m vessel), the GSD ≤ 7
m plot is most relevant.
232
Figure 11. (a) Number of accesses in the test area during daylight hours on 2 August 2013 (local
time), restricted to sensors with GSD ≤ 7 m. Forty-nine percent of the test area is observed in this
period. The number of accesses on 3, 4, and 5 August are shown in panels b, c, and d, when 42%,
32%, and 43% of the area was observed, respectively. The histogram scaling is the same on all four
plots.
corner of each plot, and the performance if night-time IR observations were possible is
shown in parentheses (night-time observations are not represented as points on the tracks).
We address the statistical effects of cloud cover in this case, briefly, in Section 6.1.
Based on the discussion in Section 2.1.1, it is assumed that a 21 m vessel like Niña
would require sensors with GSD ≤ 7 m for detection, and hence, two contacts could have
been made during this 4 day period, separated by 537 km with no additional night-time
contacts even if IR capability was available. Figure 11 shows the number of accesses for
each point in the test area for sensors with GSD ≤ 7 m during daylight hours only, on 2
August (main panel) and in the 3 days following. Approximately 50% of the area is
imaged at least once during this period.
6. Discussion
The ‘constellation’ of 54 spacecraft produces complete coverage of the test area, with 80
or more separate observations of each point taking place in a 9-day period (Figure 6).
Figure 2 shows that 19 sensors offer GSD > 200 m. As described in Section 2.1.1,
GSD ≤ 152 m is required to detect the largest ship currently in service assuming three
pixels are required for a detection, but sensors with GSD up to 400 m have been
considered for exceptional circumstances in which lighting and sea surface conditions
might permit single-pixel detection of a large vessel, or several pixels covering a vessel
wake, to be useful.
Lower angular resolution is typically accompanied by wider fields of view, so sensors
with larger GSD make a significant contribution to coverage extent and frequency.
Nevertheless, Figure 7 shows that even when restricted to sensors with GSD ≤ 30 m,
233
approximately 95% of the area receives at least one visit per day during daylight hours,
and the majority of the area is visited four or more times each day. For sensors with
GSD ≤ 7 m, no more than 50% of the test area is observed daily, during daylight hours
over the period considered, and since many spacecraft have similar orbital inclination and
equator crossing times, distinct stripes of coverage and gaps emerge.
Due to the Sun-synchronous behaviour of all but one spacecraft, the coverage pattern
moves westward by approximately 2100 km over the average orbital period (98.5 min) at
mid-latitudes, compared to the test area dimensions of approximately 926 km × 926 km,
so each sensor passes over the area no more than once per day. But because there are not
integer numbers of orbital periods in a solar day, and since there is a range of periods
within the constellation, significant changes are observed in the pattern from day to day
(Figure 11). The speed of maritime traffic is insignificant compared to the breadth of the
pattern and the rate at which it advances so that even for small vessels the probability of a
fix being obtained per unit time is relatively insensitive to vessel speed. However, speed is
relevant in determining the search radius necessary to locate a target between fixes.
As expected, longer periods spent inside the test area tend to result in more contacts;
the four least observed targets (Morning Miracle, Lica Maersk, Laust Maersk, and Sun
Princess) spend the shortest times in the target area (between 6.7 and 17.3 hours), while
Ocean Village and British Security, the most observed, were inside the area for 141.8 and
110.0 hours respectively. Without IR observations, time of day is also significant: Sea
Princess has the same number of daytime fixes as Sun Princess despite spending ~2.5
times longer in the test area, because much of that time was at night.
6.1. Detection frequency
The purpose of including test vessels in the model was to explore the likelihood of
detecting targets exhibiting realistic behaviour with an observation system that has a
complex and varying footprint. Table 3 shows that over the 9-day simulation period,
every vessel was observed at least once at GSD ≤ 7 m. But this result is an underestimate
of the constellation’s detection capability, because vessels were included on realistic tracks
which extended beyond the test area, and most spent the majority of the 9-day period
outside of that area, where no observations were logged. This is accounted for in column 6
of Table 3, which gives the period between observations inside the test area (which, in the
case of a single fix, is the same as the total time spent in the area). As a function of GSD,
the average periods are 10.1 hours (GSD ≤ 200 m), 11.2 hours (GSD ≤ 50 m), 18 hours
(GSD ≤ 25 m), and 31 hours (GSD ≤ 17 m), although uncertainty in these values is high
given the limited number of targets considered. Note that these gaps are longer than the
maximum gap and average response times plotted in Figure 8, because they depend on the
probability of observing a specific point at a specific time (i.e. the time at which the vessel
arrives at the point), while Figure 8 reflects the probability of observing a point at any
time. Large vessels are visible to the majority of sensors and are therefore likely to be
detected at least one to two times per day as reflected in Table 3. Smaller vessels are
visible only to the highest-resolution sensors, so unvisited areas appear for these targets in
daily coverage plots such as those in Figure 11.
Despite these gaps, the results suggest that even when restricted to GSD ≤ 17 m
observations, a given vessel will be detected every 1–4 days. Although far from comprehensive, this coverage may still be significant: approximately 3 days separate the two
high-resolution observations of Ship 10, compared to the 8 days which elapsed between
the last known position of the Niña and reporting her overdue. The availability of
234
EO-SBMDA imagery may have enabled a more precisely focused search area to be
defined, although whether this would have resulted in locating the crew is impossible to
determine. It is important to note, however, that the satellite imagery provided to volunteer
searchers via TomNod was acquired approximately 1 month after the vessel was reported
missing and was obtained through the efforts of DigitalGlobe who undertook a campaign
of targeted imagery over 500,000 km2 of ocean at this time. The power of the proposed
EO-SBMDA system is that it is passive and operates continuously; images over water are
taken and stored for some finite time, so that in the case of Niña, all images of the area
between the North Island and Newcastle, Australia, taken at the time of the voyage using
instruments with the required GSD could be retrieved and analysed manually and/or using
the automated techniques described by, for example, Corbane, Marre, and Petit (2008);
Corbane et al. (2010) and Yang et al. (2014).
Cloud-free conditions are critical for imaging, and an objection may be raised that the
EO-SBMDA concept is rendered inoperative at precisely those times when vessels are at
maximum risk. However, while the probability of capturing a vessel during these critical
hours is consequently low, the availability of a position fix from the constellation in the
hours or days prior to an incident is still of potential significance in reducing the
uncertainty in location and constraining any search area. Using the case of Niña as an
example, weather history indicates that there were 116 hours of ‘bright sunshine’ over
Auckland in June 2013 (National Institute of Water & Atmospheric Research Ltd. 2013).
The average length of day (sunrise–sunset) for Auckland in June is approximately 9 hours
45 min, suggesting that a line of sight from a spacecraft to the ground was available for
~40% of the time.
6.2. Detection avoidance
The intent of a vessel is significant when evaluating the effectiveness of the EO-SBMDA
constellation. Although the coverage pattern is complex and time-varying, its general
form is predictable using publicly available data. If the operator of a vessel wishes to
evade detection, then modelling can be used to plot a route, which minimizes the
probability of detection by placing the vessel in an observation gap during each satellite
overpass. This is most easily accomplished for small vessels. But the detailed coverage
pattern is difficult to predict with sufficient precision to guarantee non-detection, because
unlike the current work which assumes fixed instrument pointing, in reality instruments
can be pointed within a finite FOR, which modifies the footprint, and detailed a priori
pointing information is not generally available to the public. Another more practical
approach to detection avoidance would be to bring a vessel to a standstill around 10:30
local time when the majority of the assets pass overhead. This tactic would minimise
production of a wake, and so reduce the signature that might be exploited to assist in
detecting smaller vessels.
6.3. Resource demands
The proposed approach exploits assets that are already in service. In many cases, these
spacecraft do not undertake image acquisition over oceans, since revenue is typically
earned by generating imagery of targets on land. But even though the systems may be
untasked over oceans, extending imaging operations to cover maritime regions introduces
costs and penalties in power, data storage and telemetry requirements.
235
Imager power requirements vary significantly depending on factors including focal
plane technology, size, and cooling requirements but are typically between a few to a few
hundred watts. Spacecraft power budgets take into account subsystem power consumption
and duty cycles, which, in a power-limited design, are dictated by factors including the
characteristics of the power supply. For example, the REIS imager on RapidEye has a
power consumption of 93 W (Jena Spaceborne 2012), but the spacecraft solar arrays
provide ~100 W in sunlight (Tyc et al. 2005), and based on the expected eclipse duration,
the orbit-averaged power is likely to be of order ~60 W. Continuous imager operation is
therefore not feasible. On-board data storage, processing, and downlink availability also
limit the area that can be captured per orbit. In the case of RapidEye, each satellite has
sufficient storage to capture a 1500 km-long swath per orbit (Jung-Rothenhäusler,
Weichelt, and Pach 2007), and the system of five satellites is designed to capture a total
of 4 million km2 per day (Schulten et al. 2009).
Downlink and storage requirements are significant. To estimate the data volume
generated in the NZEEZ test case, the first and last contact times during each pass over
the test zone are logged, for all sensors, over the simulation period. The resulting swath
length is estimated by multiplying the duration of observation by the satellite’s ground
speed (estimated using the data for orbital period in Table 1 – the average speed is found
to be ~6.8 km s−1). The swath width is obtained from the STK model using the
geographical limits of the footprint boundary and agrees with the product of angular
field of view multiplied by altitude (Figure 3). The area of the swath is then calculated and
divided by the square of the sensor GSD value to obtain the number of pixels in the
image. Finally, the data volume for the swath is obtained by multiplying the number of
pixels by the assumed bit depth of the image. The total data storage requirement for the
constellation is the sum of these swath data volumes, for every access and every sensor.
This approach assumes that no cropping is performed, so that even in the case of a single
pixel falling within the test zone, the complete swath including all pixels outside the test
area is stored.
This approach can be verified using RapidEye as an example, since detailed data
product specifications are published online (BlackBridge 2013). The REIS sensor has a
footprint with a cross-track dimension of approximately 77 km, an along-track unit of
length 25 km, and a GSD of 6.5 m as adopted elsewhere in this work. Hence there are
(77 × 25) km2/(6.5 × 6.5) m2 = 53,472,222 pixels in a 77 km × 25 km image. A single
77 km × 25 km image at the quoted 16 bit depth therefore has a size of ~102 MB.
BlackBridge (2013) (Table 3) quotes a single frame file size of 462 MB for five
wavebands, while the preceding estimate is based on a single waveband. Hence, the
file size for a single band, based on the published specification, is 462/5 = 92.4 MB or
91% of the value estimated here. Since our estimation ignores potential size reduction
due to lossless compression or reduced image resolution at different wavelengths, it is
broadly consistent with the published values.
For the purposes of this study, we have assumed a 16-bit depth for the raw data
produced by all sensors, with observations in a single waveband. For the 9-day simulation
period, the average raw data volume generated per sensor per pass was 7 × 1010 bits, with
minimum and maximum values of 1.3 × 107 and 1.3 × 1012 bits, respectively, where
higher volumes are typically associated with smaller GSD sensors. The total volume of
raw data generated over the simulation period is 3.9 TB, equivalent to approximately 90
complete images of the test zone at 6 m GSD and 16-bit depth. This is broadly consistent
with the number of accesses recorded (Figure 6, right). However, we note that on-board
data compression techniques (e.g. Yu, Vladimirova, and Sweeting 2009) can be used to
236
reduce the volume of data transmitted, in some cases reducing the final bit depth to 10 or
11 bits. In the case of compression to 10 bit depth, the total volume of data generated over
the period of the simulation would be 2.4 TB. In practice, the application of error
correction codes will increase this volume, and so the volume of transmitted data is likely
to lie between these limiting cases. The daily raw (16 bit) and compressed (10 bit) data
volumes generated by the constellation, excluding error correction overhead, are shown in
Figure 12.
Resources are therefore a significant consideration in the implementation of any EOSBMDA system and pose a particular challenge when using existing spacecraft that were
not designed with this role in their performance requirements. Assessing the resource
capacity of each spacecraft in Table 1 is beyond the scope of the current work, but the
example of RapidEye demonstrates that implementation of an EO-SBMDA system is not
simply a case of keeping imagers active while over water. Nevertheless, a practical system
based on existing assets may still be possible by, for example, prioritizing the most
resource-limited imagers to regions such as sea lanes (the most commonly used routes),
conservation zones and protected fisheries, waters around security-critical coastlines, or
specific events such as maritime races.
The issue of downlink and data storage/processing capacity is critical. The diverse set
of spacecraft identified in this work use a wide variety of nationally and privately operated
ground stations. A practical EO-SBMDA system presents two fundamental challenges to
that model: (1) a significant increase in the volume of data to be transmitted to ground and
(2) the requirement for efficient sharing and processing of data from all of the assets. The
Figure 12. Daily data volumes for the simulation (in units of bits). Dark grey bars indicate the
volume based on raw 16-bit data, and light grey bars represent the volume following on-board
processing, which is assumed to reduce the data to 10-bit depth. Error correction overheads are not
considered. Note that the simulation covers 9 × 24 hour periods, and so days 1 and 10 cover less
than 24 hours.
237
test zone accounts for only ~0.2% of the area of the world’s oceans, so that the volume of
data generated by a whole-ocean EO-SBMDA system using the current assets is likely to
be several hundred terabytes per day before compression and other efficiency-improving
measures are accounted for. Widespread cooperation between ground stations is required
if the EO-SBMDA concept is to operate on this basis, and prioritization of monitoring
areas is likely to be necessary to limit the amount of data transmitted. The concept of a
federated network of ground stations is considered by Spangelo, Boone, and Cutler (2010)
in the context of the communication demands arising from the increasing number of
small-satellite missions in flight or under development, and the work considers the role
that might be played by the significant numbers of smaller stations owned by universities
in such a scheme. Just as ‘crowdsourcing’ approaches have been applied to the issue of
image data analysis, a similar approach using distributed but cooperative stations may
have an important part to play in the implementation of the EO-SBMDA concept.
The detailed ground segment architecture for EO-SBMDA will be considered in future
studies but is likely to include a combination of large numbers of small ground stations,
with system coordination provided by one or more large ground installations that will also
host the EO-SBMDA data centre. The design of the space segment must also be
considered in this respect; for example, the implementation of efficient on-board processing may enable a reduction in the volume of data to be transmitted, while the availability
of efficient inter-satellite links may permit, e.g., selective omission of an area that has
been imaged within a predetermined time to avoid excessive duplication of data, or
relaying of high priority data from spacecraft with no direct line of sight to a ground
station.
7. The future: nanosatellites, dedicated systems, and the search for MH 370
This study has shown that useful levels of maritime coverage may be possible using
current, commercially accessible EO imaging spacecraft, but that there are aspects of the
effective constellation which are not ideal for this purpose. The clustering of spacecraft
around mid-morning equator crossing times (Figure 5) reflects the primary mission
requirements of most of the spacecraft, where imagery showing good surface relief at
times of maximum atmospheric clarity is of prime importance, but results in large coverage gaps in the afternoon and early morning. Further, the power- and data-handling
demands introduced by extending imaging operations into the wider maritime domain
are nontrivial, limiting the ability of some, if not all, of the currently available assets to
image large areas of water.
The usefulness of high-resolution EO imagery for maritime domain awareness is
evidenced by the references cited in the current work. Studies have been conducted into
the design of a constellation of satellites that are purpose-designed to provide responsive
imaging; for example Krueger et al. (2009) consider the design of a constellation of
between 4 and 16 spacecraft for this purpose. However, the ultimate goal of the EOSBMDA concept described here is to provide an image history for the maritime domain,
rather than to provide imagery in response to an alert. Thus, in the case of, for example,
the loss of Niña, observations of the vessel taken shortly before she encountered difficulties could be identified and retrieved from the image bank to direct the search, rather than
tasking spacecraft with searches for the vessel or debris after the event only when she was
reported overdue. With the increasing capability of small satellite platforms, a dedicated
EO-SBMDA constellation providing this capability may be feasible. The high-resolution
imaging satellites of the RapidEye constellation, for example, are based on the Surrey
238
Satellite Technology Ltd. (SSTL) 150 platform (Stoll et al. 2012; Baker, Davies, and
Boland 2008), which has a maximum payload mass of 50 kg and dimensions of approximately 0.9 m × 0.8 m × 0.7 m, while NigeriaSAT2 is based on the SSTL-300 platform,
which offers 150 kg payload mass but still occupies a volume less than 0.8 m3. So
dedicated EO-SBMDA satellites need not be large, bespoke spacecraft but can instead be
based on smaller, off-the-shelf systems. Evening Star, a British satellite-based imaging
system currently under study, is designed to exploit the increasing capabilities of constellations of these small satellites to provide timely observation on a global basis, for
applications including maritime security (Eves, Personal Communication, 2014).
Recent developments in the nanosatellite sector are also relevant. Private companies
including Skybox Imaging (Skybox Imaging 2014) and Planet Labs (Planet Labs Inc
2014), both in the USA, are now operating significant numbers of commercial, highresolution imaging nanosatellites that promise substantial improvements in the performance of the EO-SBMDA constellation (see, e.g. Kumagai 2014). In February 2014,
Planet Labs began launching the first of a fleet of over 100, 3-unit CubeSats from the
International Space Station (ISS), each carrying a high-resolution (GSD ~ 4.4 m) imaging
system (Marshall and Boshuizen 2013). This set of assets, known collectively as Flock-1,
will be spaced out along an orbit sharing the 51° inclination of the ISS and is thus not
Sun-synchronous. To date, Planet Labs has received $65 million in funding to build and
deploy its constellations of CubeSat imagers, while Skybox was purchased by Google for
$500 million, evidencing substantial financial interest in increasing the availability of
commercial electro-optic imagery from space (Morring 2014). As noted earlier, highresolution imagers offer narrow fields of view; nevertheless, the addition of such a large
number of high-resolution imagers into non-Sun Synchronous orbits can be expected to
reduce the mid-morning–afternoon coverage gap for equatorial and mid-latitudes, as well
as increasing the coverage possible within the main band of assets identified in this work.
The low cost of these systems compared to conventional large platforms offers the
possibility of developing and deploying a satellite constellation with resources tailored
to the EO-SBMDA system at a small fraction of the cost of more traditional designs.
At the opposite end of the spectrum of platform size, the Canadian company
UrtheCast installed two high-resolution cameras (THEIA, with 6.2 m GSD, and IRIS,
with GSD better than 1 m) on the ISS on 27 January 2014 (UrtheCast Corp 2014;
Kumagai 2014). Flying imagers on a platform as large as ISS removes some of the
resource limitations present in smaller spacecraft, as described in Section 6.3. UrtheCast
plan to use the constant ground link available on the ISS to provide near real-time imagery
and high-resolution video over land and sea, with the 100% duty cycle (i.e. all day, every
day) that EO-SBMDA requires for maximum efficacy. While the inclination of the ISS
limits the resolution in imagery of very high northern and southern latitudes due to large
slant-ranges, the ground track covers some of the world’s busiest shipping lanes and
sensitive marine conservation areas, and this approach has the potential to make a major
contribution to an EO-SBMDA system.
ESA’s Sentinel-2 mission currently under development could also provide a very
significant contribution to EO-SBMDA. Sentinel-2 consists of two 1.2 ton polar-orbiting
satellites in Sun-synchronous orbits at 786 km altitude with 10:30 equator crossing times,
sharing the same orbit but spaced ~180 apart. Each spacecraft will offer imagery at up to
10 m GSD, and the mission will ‘systematically acquire observations over land and
coastal areas from – 56 to 84 latitude including islands larger than 100 km2, EU islands,
all other islands less than 20 km from the coastline, the whole Mediterranean Sea, all
inland water bodies and all closed seas’ (Drusch et al. 2012, 26). This capability,
239
combined with low data latency (<2 hours), will make Sentinel-2 an addition of major
importance to any future EO-SBMDA system.
Funding for the collection and processing of EO-SBMDA data may ultimately derive
from a myriad of unrelated sources and channels: government, quasi-government, nongovernmental, commercial, and not-for-profit and private investors. The aggregation of
resources may be serendipitous, as by-products from research proposals under programmes such as the European Union’s Horizon 2020, specifically within the call for
proposals under EARTH OBSERVATION-2015-LEIT SPACE: bringing EO applications
to the market (EO-1-2015) and Technology developments for competitive imaging from
space (EO-3-2015) (European Commission 2014). In addition, funding might be secured
under the IMO’s Technical Cooperation Fund, Multi-Donor Trust Funds, and Bi-lateral
agreements (International Maritime Organisation 2014).
Finally, while the scope of this research did not include the disappearance, on 8 March
2014, of Malaysian Flight MH 370, we contend that if a collaborative EO-SBMDA
constellation had been in place, imagery could have been collected and analysed in a
timely fashion, potentially reducing the 7.68 million km2 search area (Johnson and Lu
2014). With the level of coverage currently available, the probability of capturing the
aircraft in flight is extremely low. However, as the response time analysis earlier has
shown, the probability of a given area being observed within a few hours of an impact is
very high, offering the possibility of imaging debris fields on the ocean surface before
significant dispersal takes place. Imagery acquired by the Japanese MTSAT-1R spacecraft
on 8 March 2014 (National Oceanic and Atmospheric Administration 2014) (visible
imaging channel GSD 1.0 km) shows significant areas of clear sky or partial cloud to
the west of Australia, around the area where flight data recorder-like pings were detected
by the search vessels Ocean Shield and Hai Xun in April 2014. The availability of images
covering potential flight paths, or the ping locations, at the time of the disappearance, may
have been of significant assistance in the search for the aircraft.
8. Conclusions
We have described the potential of a system to provide space-based maritime domain
awareness using commercially accessible data from high-resolution imaging spacecraft.
Our analysis indicates that the combined observations of high-resolution imaging sensors
carried on 54 commercially accessible spacecraft currently in service are capable of
providing one or more position fixes every day, for large vessels (length ≥ 100 m), and
our assumptions on the resolution required to detect vessels are found to be consistent
with the results of practical observations detailed in the literature (Corbane, Marre, and
Petit 2008). It is found that the combined observations of the sensors in the constellation
provide sufficient coverage to yield one detection of a smaller (~20 m) vessel every
1–4 days, the reduced coverage being a consequence of the smaller fields of view of
higher-resolution imagers. Although coverage is currently insufficient to provide comprehensive tracking of small vessels, consideration of the case of the 21 m-long schooner
Niña suggests that the existing constellation could play an important role in reducing the
area covered when searching for vessels in difficulty.
The concept relies on spacecraft operators extending the duty cycle of imaging
activities to cover ocean data collection, either globally or over areas of specific interest
such as shipping lanes and the exclusive economic zones in national waters, and this is
likely to limit the amount of coverage that can be provided by existing spacecraft to
higher priority regions rather than the global approach, which is the ultimate goal of the
240
concept. However, the new generation of small satellite and nanosatellite platforms offers
an opportunity to implement a dedicated EO-SBMDA system with resources adequate to
meet these demands, and studies into this approach are currently in progress.
To provide adequate downlink opportunities, a cooperative approach is likely to be
required between large numbers of ground stations including small installations owned by
private organisations and universities, introducing a crowd-sourced approach to the
operation of the system. However, a small number of large ground station facilities may
provide a substantial fraction of the total downlink capacity and perform a coordinating
role for the system. The model we propose is one in which image data are stored at the
major coordinating ground station facilities, to be retrieved for automated analysis when
the need to locate or track a vessel is identified; analytical processes applicable to such a
system have been described by several authors (Corbane et al. 2010; Yang et al. 2014),
and may be implemented on site at the coordinating facilities, or through a cloudcomputing approach using an automated image analysis pipeline, with the option of
manual inspection (as implemented by, e.g., the TomNod website) as a second line of
investigation.
The EO-SBMDA concept requires new levels of cooperation to be implemented
between international satellite operators and ground segment providers. But the benefits
of adopting this approach, and of implementing the crowd-sourcing approach to spacecraft
operations for the first time, the opportunity exists to use high-resolution optical imaging
from space as a highly effective method of improving the safety and security of life at sea.
Acknowledgements
We appreciate the guidance and support given to us in the execution of this project by Dr Charles, J.
Holland, Associate Director, ONR Global. We are also grateful to Dr Brian Young, Dr Sally Garrett,
and Dr John Kay (New Zealand Defence Technology Agency), for their valuable inputs that have
contributed to this work, including definition of the test area and assistance in obtaining vessel track
data used in the models and subsequent analysis, and to Dr Stuart Eves (Airbus Defence & Space)
for useful discussions during the course of the project. We thank DMC International Imaging Ltd
and particularly Kim Wilson and Dave Hodgson for their interest in and support for this work.
Finally, we thank the referees for their comments and suggestions, which have led to significant
improvements in the manuscript.
Funding
This research project was suggested by the US Office of Naval Research (Global) and funded by
ONRG through NICOP Research Grant N62909-13-1-N137.
ORCID
N.P. Bannister
http://orcid.org/0000-0001-7849-9102
References
Agnew, M., L. Renouard, and A. Hegyi. 2012. “EDRS – SpaceDataHighway: Near-Real-Time Data
Relay Services for LEO Satellites and Haps.” 30th AIAA International Communications
Satellite System Conference, ICSCC.
Aorpimai, M., and P. L. Palmer. 2007. “Repeat-Ground Track Orbit Acquisition and Maintenance for
Earth-Observation Satellites.” Journal of Guidance, Control, and Dynamics 30 (3): 654–659.
doi:10.2514/1.23413.
241
Baker, A. M., P. Davies, and L. Boland. 2008. “Moving Towards Commercial Earth Observation
Services with Small Satellite Constellations.” In Small Satellites for Earth Observation, edited
by R. Sandau, H.-P. Röser, and A. Valenzuela, 57–66. Dordrecht: Springer.
BlackBridge. 2013. “Satellite Imagery Product Specifications.” Accessed April 2, 2014. http://www.
blackbridge.com/rapideye/upload/RE_Product_Specifications_ENG.pdf
Bruno, M., K. W. Chung, H. Salloum, A. Sedunov, N. Sedunov, A. Sutin, H. Graber, and P. Mallas. 2010.
“Concurrent Use of Satellite Imaging and Passive Acoustics for Maritime Domain Awareness.” In
Waterside Security Conference (WSS), 2010 International, 1–8. Piscataway, NJ: IEEE.
Carson-Jackson, J. 2012. “Satellite AIS – Developing Technology or Existing Capability?” Journal
of Navigation 65 (2): 303–321. doi:10.1017/S037346331100066X.
Committee on Earth Observation Satellites. 2014. “The Earth Observation Handbook.” Accessed
August 12, 2014. http://www.eohandbook.com
Corbane, C., F. Marre, and M. Petit. 2008. “Using SPOT-5 HRG Data in Panchromatic Mode for
Operational Detection of Small Ships in Tropical Area.” Sensors 8 (5): 2959–2973.
doi:10.3390/s8052959.
Corbane, C., L. Najman, E. Pecoul, L. Demagistri, and M. Petit. 2010. “A Complete Processing
Chain for Ship Detection Using Optical Satellite Imagery.” International Journal of Remote
Sensing 31 (22): 5837–5854. doi:10.1080/01431161.2010.512310.
Detsis, E., Y. Brodsky, P. Knudtson, M. Cuba, H. Fuqua, and B. Szalai. 2012. “Project Catch: A
Space Based Solution to Combat Illegal, Unreported and Unregulated Fishing: Part I: Vessel
Monitoring System.” Acta Astronautica 80: 114–123. doi:10.1016/j.actaastro.2012.06.009.
[DigitalGlobe(2013)] DigitalGlobe. 2013. “Satellite Imagery and the Crowd Join Hands to Help
Locate the Nina.” DigitalGlobe Blog. Accessed August 12, 2014. http://www.digitalglobeblog.
com/2013/09/01/crowdsourcingnina/
Donati, J., and D. Fineren. 2012. “Iran Falsifying AIS Data to Conceal Ship Movements.” Reuters.
Accessed April 1, 2014. http://www.reuters.com/article/2012/12/06/us-syria-iran-trackingidUSBRE8B50KX20121206
Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P.
Laberinti, P. Martimort, A. Meygret, F. Spoto, O. Sy, F. Marchese, and P. Bargellini. 2012.
“Sentinel-2: Esa’s Optical High-Resolution Mission for GMES Operational Services.” Remote
Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026.
eoPortal. 2014. “Earth Observation Portal.” Accessed August 12, 2014. https://eoportal.org/web/eoportal
European Commission. 2014. “H2020-EO-2015.” Accessed August 19, 2014. http://ec.europa.eu/
research/participants/portal/desktop/en/opportunities/h2020/calls/h2020-eo-2015.html
Flannery, J. 2013. “The Disappearance of the Nina.” Sail Magazine. Accessed August 12, 2014.
http://www.sailmagazine.com/nina
Greidanus, H., and N. Kourti. 2006. “Findings of the DECLIMS Project–Detection and
Classification of Marine Traffic from Space.” In Proceedings of SEASAR, Noorwijk, edited
by H. Lacoste, 23–26. ESA.
International Maritime Organisation. 2014. “Funding Sources.” Accessed August 19, 2014. http://
www.imo.org/OurWork/TechnicalCooperation/ITCP/Pages/Funding.aspx
Irvine, J. M., and W. G. Fishell. 1997. “National Imagery Interpretability Rating Scales (NIIRS):
Overview and Methodology.” Airborne Reconnaissance XXI 3128: 93–103. doi:10.1117/
12.279081.
Jakhu, R. S. 2010. National Regulation of Space Activities. Vol. 5. New York: Springer.
Jena Spaceborne Scanner JSS 56. 2012. “Jena-Optronik GmbH.” Accessed April 1, 2014. http://
www.jena-optronik.de/en/spaceborne-instruments/jena-spaceborne-scanner.html
Johnson, R., and D. Lu. 2014. “The Scale of the Search for Flight MH370.” The Washington Post.
Accessed October 13, 2014. http://www.washingtonpost.com/wp-srv/special/world/malaysianair-scale/
Jung-Rothenhäusler, F., H. Weichelt, and M. Pach. 2007. “Rapideye. A Novel Approach to Space
Borne Geo-Information Solutions.” Proceedings of the ISPRS Hanover Workshop, International
Society for Photogrammetry and Remote Sensing, Hanover, May 29–June 1.
Krueger, J. K., D. Selva, M. W. Smith, and J. Keesee. 2009. “Spacecraft and Constellation Design for a
Continuous Responsive Imaging System in Space.” AIAA Space 2009 Conference & Exposition,
American Institute of Aeronautics and Astronautics, Pasadena, CA, September 14–17.
Kumagai, J. 2014. “5 Earth-Imaging Start-Ups Coming to a Sky Near You [News].” IEEE Spectrum
51 (4): 20–21. doi:10.1109/MSPEC.2014.6776288.
242
Leachtenauer, J. C., W. Malila, J. Irvine, L. Colburn, and N. Salvaggio. 1997. “General ImageQuality Equation: GIQE.” Applied Optics 36 (32): 8322–8328. doi:10.1364/AO.36.008322.
Lundquist, E. 2010. “Maritime Domain Awareness: To Know and To Act.” RUSI Defence Systems.
Accessed April 1, 2014. https://www.rusi.org/downloads/assets/lundquist_RDS_feb2010.pdf
MapMart. 2014. “Mapmart Global Mapping Solutions.” Accessed August 12, 2014. http://www.
mapmart.com
Marshall, W., and C. Boshuizen. 2013. “Planet Labs Remote Sensing Satellite System.” Cubesat
Developers Workshop, Logan, UT, 10–15 August 2013.
Meier, P. 2012. “Crisis Mapping in Action: How Open Source Software and Global Volunteer
Networks Are Changing the World, One Map at a Time.” Journal of Map & Geography
Libraries 8 (2): 89–100. doi:10.1080/15420353.2012.663739.
Mills, S., A. Kjeldsen, and J. Shipp. 1998. “INFEO Search-A.” European Space AgencyPublications-ESA SP 422: 489–500.
Morring, F. Jr. 2014. “Smartphone Advances Drive Smallsats.” Accessed August 19, 2014. http://
aviationweek.com/space/smartphone-advances-drive-smallsats
National Institute of Water & Atmospheric Research Ltd. 2013. “Climate Statistics June 2013.” NIWA.
Accessed April 1, 2014. https://www.niwa.co.nz/sites/niwa.co.nz/files/climate_statistics__june_2013.pdf
National Oceanic and Atmospheric Administration. 2014. “NOAA Satellite and Information Service.”
Accessed October 13, 2014. http://www.ncdc.noaa.gov/gibbs/image/MTS-2/IR/2014-03-08-09
Norris, P. 2011. “Developments in High Resolution Imaging Satellites for the Military.” Space
Policy 27 (1): 44–47. http://www.sciencedirect.com/science/article/pii/S0265964610001098
Park, S. C., M. K. Park, and M. G. Kang. 2003. “Super-Resolution Image Reconstruction: A
Technical Overview.” IEEE Signal Processing Magazine 20 (3): 21–36. doi:10.1109/
MSP.2003.1203207.
Planet Labs Inc. 2014. “Welcome to Your Planet - Planet Labs.” Accessed August 12, 2014. https://
www.planet.com
Ports of Auckland Limited. 2014. “Schedules.” Accessed August 12, 2014. http://www.poal.co.nz/
shipping_cargo/schedules.htm
Posada, M., H. Greidanus, M. Alvarez, M. Vespe, T. Cokacar, and S. Falchetti. 2011. “Maritime
Awareness for Counter-Piracy in the Gulf of Aden.” In Geoscience and Remote Sensing
Symposium (IGARSS), 2011 IEEE International, 249–252. Piscataway, NJ: IEEE.
Re, E., V. Boissinot, A. Ginesi, and C. Tobehn. 2012. “A Simple High Precision Method for
Extrapolating Sat-AIS System Performance.” In Advanced Satellite Multimedia Systems
Conference (ASMS) and 12th Signal Processing for Space Communications Workshop
(SPSC), 2012 6th, 266–272. Piscataway, NJ: IEEE.
Ross, E., B. Arifin, and Y. Brodsky. 2011. “An Information System for Ship Detection and
Identification.” In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE
International, 2081–2084. Piscataway, NJ: IEEE.
Royal Navy. 2011. “Protector Catches the Whirlybird.” Navy News. Accessed 12 August, 2014.
https://navynews.co.uk/archive/news/item/1271
Sailwx.info. 2014. “Live Marine Information.” Accessed August 12, 2014. http://www.sailwx.info
Schulten, D., G. Tyc, Y. Brown, J. Steyn, N. Hannaford, and W. Larson. 2009. “RapidEye System
Commissioning and On-Orbit Performance.” 23rd Annual AIAA/USU Conference on Small
Satellites, American Institute of Aeronautics and Astronautics, Logan, UT, August 10–13.
Skybox Imaging. 2014. “Skybox Imaging.” Accessed August 12, 2014. http://www.skyboximaging.com
Spangelo, S. C., D. Boone, and J. Cutler. 2010. “Assessing the Capacity of a Federated Ground
Station.” Aerospace Conference, 2010 IEEE, March 1–9. Piscataway, NJ: IEEE.
Spyrou, A. G. 2011. From T-2 to Supertanker: Development of the Oil Tanker, 1940-2000, Revised.
Bloomington, IN: iUniverse.
Stoll, E. H. Konstanski, C. Anderson, K. Douglass, and M. Oxfort. 2012. “The Rapideye Constellation and
Its Data Products.” In Proceedings of 2012 IEEE Aerospace Conference, 1–9. Piscataway, NJ: IEEE.
Suchandt, S., H. Runge, and U. Steinbrecher. 2010. “Ship Detection and Measurement Using the
Terrasar-X Dual-Receive Antenna Mode.” In Geoscience and Remote Sensing Symposium
(IGARSS), 2010 IEEE International, July, 2860–2863. Piscataway, NJ: IEEE.
Thomas, G. 2011. “Homeland Security Starts at Sea.” Earth Imaging Journal 2: 10–13.
Tyc, G., J. Tulip, D. Schulten, M. Krischke, and M. Oxfort. 2005. “The Rapideye Mission Design.”
Acta Astronautica 56 (1–2): 213–219. doi:10.1016/j.actaastro.2004.09.029.
243
Union of Concerned Scientists. 2014. “UCS Satellite Database.” UCS. Accessed May 1, 2014. http://
www.ucsusa.org/nuclear_weapons_and_global_security/solutions/space-weapons/ucs-satellitedatabase.html
UrtheCast Corp. 2014. “UrtheCast - The Earth Video Camera.” Accessed August 21, 2014. http://
www.urthecast.com
World Meteorological Organization. 2014. “OSCAR: Observing Systems Capability Analysis and
Review Tool.” Accessed August 12, 2014. http://www.wmo-sat.info/oscar/
Wu, G., J. De Leeuw, A. K. Skidmore, Y. Liu, and H. Prins. 2009. “Performance of Landsat TM in
Ship Detection in Turbid Waters.” International Journal of Applied Earth Observation and
Geoinformation 11 (1): 54–61. doi:10.1016/j.jag.2008.07.001.
Yang, G., B. Li, S. Ji, F. Gao, and Q. Xu. 2014. “Ship Detection From Optical Satellite Images
Based on Sea Surface Analysis.” IEEE Geoscience and Remote Sensing Letters 11 (3): 641–
645. doi:10.1109/LGRS.2013.2273552.
Yu, G., T. Vladimirova, and M. N. Sweeting. 2009. “Image Compression Systems on Board
Satellites.” Acta Astronautica 64 (9–10): 988–1005. doi:10.1016/j.actaastro.2008.12.006.

Maritime domain awareness with commercially

Transcription

Similar documents

19` 4” AMF/Crestliner 1980 $2200

Introduction Safe Transfer of Technicians and Avoidance of Motion

Catalogue

Leupold - Gulf States Distributors

27` 7” Wellcraft 1990 $5900 - Chapman School of Seamanship

2003 Crownline 225 LPX - $ 19,900

The Heart

28` 6” Aquasport 1987 - Chapman School of Seamanship

Bond Movies Dr. Jonathan M Sackier As an Englishman of a certain