TRAITEMENT D'IMAGES RADAR A OUVERTURE SYNTHETIQUE DESTINE A LA DETECTION D'UN NAVIRE

PROCESSING SYNTHETIC APERTURE RADAR IMAGES FOR SHIP DETECTION

Abrégé anglais

Systems and methods relating to SAR image processing and object
detection within a SAR image. A sea clutter model in which the
texture random variable is drawn from a finite and discrete set
of values is used in the processing of SAR derived images. SAR
images are divided into sub-images, each sub-image being
processed in turn. A statistical test is applied to each sub-
image to determine whether it contains pixels representing only
non-clutter information. The statistical test is based on the
sea-clutter model, parameters of which are derived and adapted
from each sub-image. The model is designed such that it will not
permit more than a pre-determined number of false alarms. Pixels
in each sub-image with information other than clutter are
clustered, according to proximity, into object detections.
Detections from all sub-images are combined to provide global
object detection and to group clusters that may have split
across sub-image boundaries.

Revendications

We claim:
1. A method for processing a radar image to detect at least
one object in said image, the method comprising:
a) receiving said radar image;
b) dividing said image into multiple sub-images;
c) processing each sub-image by:
i) estimating parameters from said sub-image for use
in calculating a texture random variable;
ii) calculating a detection threshold for said sub-
image based on said parameters estimated in step i);
iii) for each pixel in said sub-image, determining if
said pixel contains clutter or non-clutter content
based on said detection threshold;
iv) for each pixel in said sub-image, classifying said
pixel as containing clutter or non-clutter content
based on a determination in step iii);
v) saving coordinates of each pixel containing non-
clutter content into a global set of non-clutter
pixels;
d) repeating step c) until all sub-images have been
processed;
e) processing said global set of non-clutter pixels to
result in subsets of pixels containing non-clutter content,
each subset containing pixels having non-clutter content
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from a specific object, pixels in each subset being within
a predetermined proximity to one another;
wherein said radar image is an image of a section of sea;
and
wherein said radar image is produced by a synthetic
aperture radar.
2. A method according to claim 1 wherein said method is
executed by a system on-board a satellite.
3. A method according to claim 2 wherein said satellite
contains said synthetic aperture radar.
4. A method according to claim 1 wherein said texture random
variable is a discrete random variable with a probability
density function of:
<IMG>
wherein
- a, defines a set of values that said texture random variable
can assume;
- c, defines a probability of said texture random variable being
selected randomly; and
- I is a finite number which defines a number of values in said
set of values.
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5. A method according to claim 1 wherein said subsets of
pixels are processed further to determine if non-clutter content
indicates a presence of a seaborne vessel.
6. A method according to claim 5 wherein a presence of a
seaborne vessel in said subsets of pixels generates a report of
said presence.
7. A method according to claim 5 wherein a presence of an
object other than a seaborne vessel in said subsets of pixels
generates a report of said presence.
8. A method according to claim 1 wherein said detection
threshold is calculated using:
<IMG>
where
P fa is a pre-determined false alarm rate;
.GAMMA.(.cndot.) represents a gamma function;
.GAMMA.(.cndot.,.cndot.) represents an incomplete gamma function;
n denotes a number of independent samples averaged;
.THETA. denotes a vector containing all unknown parameters;
.sigma.c2 denotes a clutter noise power level; and
.sigma.n2 denotes a thermal noise power level.
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9. A system for processing radar images, the system
comprising:
- an input module for receiving a radar image;
- an image divider module for dividing said radar image
into sub-images;
- a non-clutter detection module for processing sub-images
derived from said input radar image, said detection module
determining if pixels in a sub-image contains clutter or
non-clutter information;
- a clustering module for determining a location of pixels
containing non-clutter information in said sub-images and
for creating subsets of pixels containing non-clutter
information, pixels in a subset being within a
predetermined distance from other pixels in said subset;
wherein
- said non-clutter detection module processes each of said
sub-images by calculating a detection threshold based on
parameters from said sub-image and comparing information
from each pixel in said sub-image with said detection
threshold.
10. A system according to claim 9 wherein said parameters are
for calculating a texture random variable.
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11. A system according to claim 10 wherein said texture random
variable is a discrete random variable with a probability
density function of:
<IMG>
wherein
- a i defines a set of values that said texture random variable
can assume;
- c i defines a probability of said texture random variable being
selected randomly; and
- I is a finite number which defines a number of values in said
set of values.
12. A system according to claim 9 wherein said radar image is
produced by a synthetic aperture radar.
13. A system according to claim 9 wherein said radar image is
an image of a section of open water.
14. A system according to claim 9 wherein said system is
onboard a satellite.
15. A system according to claim 14 wherein said system is on-
board a satellite containing said synthetic aperture radar.
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16. A system according to claim 9 wherein said detection
threshold is calculated using:
<IMG>
where
P fa is a pre-determined false alarm rate,
.GAMMA.(.cndot. represents a gamma function;
.GAMMA.(.cndot.,.cndot.) represents an incomplete gamma function;
n denotes a number of independent samples averaged;
.THETA. denotes a vector containing all unknown parameters;
.sigma.c2 denotes a clutter noise power level; and
.sigma.n2 denotes a thermal noise power level.
17. Non-transitory computer readable media having encoded
thereon computer readable and computer executable instructions
which, when executed, implements a method for processing a radar
image to detect at least one object in said image, the method
comprising:
a) receiving said radar image;
b) dividing said image into multiple sub-images;
c) processing each sub-image by:
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i) estimating parameters from said sub-image for use
in calculating a texture random variable;
ii) calculating a detection threshold for said sub-
image based on said parameters estimated in step i)
iii) for each pixel in said sub-image, determining if
said pixel contains clutter or non-clutter content
based on said detection threshold;
iv) for each pixel in said sub-image, classifying said
pixel as containing clutter or non-clutter content
based on a determination in step iii)
v) saving coordinates of each pixel containing non-
clutter content into a global set of non-clutter
pixels;
d) repeating step c) until all sub-images have been
processed;
e) processing said global set of non-clutter pixels to
result in subsets of pixels containing non-clutter content,
each subset containing pixels having non-clutter content
from a specific object, pixels in each subset being within
a predetermined proximity to one another;
wherein said radar image is an image of a section of sea;
and
wherein said radar image is produced by a synthetic
aperture radar.
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18. Non-transitory computer readable media according to claim
16 wherein said texture random variable is a discrete random
variable with a probability density function of:
<IMG>
wherein
- a i defines a set of values that said texture random variable
can assume;
- c i defines a probability of said texture random variable being
selected randomly; and
- I is a finite number which defines a number of values in said
set of values.
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Description

CA 02897541 2015-07-17
Attorney Docket No. 1004P029CA01
PROCESSING SYNTHETIC APERTURE RADAR IMAGES FOR SHIP DETECTION
TECHNICAL FIELD
The present invention relates to SAR image processing. More
specifically, the present '.nvention relates to systems and
methods for processing images obtained by a synthetic aperture
radar (SAR) to detect objects in the images.
BACKGROUND OF THE INVENTION
Advances in radar and image processing technology have provided
the world with the ability to image the world from space.
Satellite based synthetic aperture radar (SAR) allows images of
the Earth to be taken from outer space with resolutions of up to
mere meters. This allows an unprecedented opportunity for
surveillance of potential seaborne threats to coastlines and
harbours. However, current technology requires prodigious
amounts of processing before clear images of ships and seaborne
artefacts can be derived from SAR images.
Currently, vessel detection based on single¨polarised SAR images
is achieved through a statistical detection step in which a ship
detection is declared when a pixel magnitude exceeds a
predetermined threshold. The threshold is computed based on a
statistical model of the measured SAR magnitude sea background
data, or in radar terms, the clutter. Currently, virtually all
operational space-based SAR vessel detectors employ minor
variants of two statistical clutter models: a) a Gaussian-
distribution based model or b) the K-distribution based model,
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which is based on a composite model taking into account
inhomogeneity in the background texture.
Once normalized to its average magnitude value, the distribution
functions are parameterized through a-priori unknown parameters
which are adaptively estimated to fit the measured data. For
Gaussian clutter, one parameter is a scaled variance and for the
K-distribution, one is a txture parameter. The estimated values
for these parameters are subsequently inserted into the model to
determine the desired detection threshold.
Although the Gaussian distribution is widely used for the
clutter in low-resolution SAR images, it is an inaccurate model
unless a larger number of independent pixels are averaged, which
is impractical as it would severely reduce the target SNR and
hence its detectability. For commonly used single-look images,
the assumption of Gaussian clutter breaks down. This is
especially true for high-resolution imagery where the radar
essentially resolves some of the large-scale structures of the
sea surface and thereby becomes heterogeneous (i.e. non-Gaussian
distributed).
The more sophisticated K-distribution model incorporates this
texture inhomogeneity by utilizing a second independent
multiplicative texture random variable. Although more physically
sensible than the Gaussian model, the K-distribution model is
inherently based on the assumption that unavoidable thermal
white noise caused by the electronic system components is
negligible. However, this assumption is only justified for high
power levels (i.e. when the clutter power level is significantly
larger than the thermal noise), such as for airborne SAR systems
which involve available large transmit power and relatively
short stand¨off ranges. For space-based SAR, however, this
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assumption is generally not valid, manifesting itself in a
deviation of the anticipated K-distribution model from the
measured data. This deviation will, in principle, result in an
overestimation of the detection threshold, potentially leading
to many missed targets such as those that are smaller and hence
have weaker reflection. This is more pronounced in heterogeneous
clutter caused by high sea states. Further, the mathematical
description of the K-distribution function involves highly
nonlinear functions (e. g. Bessel-functions), which makes the
adaptive estimation of the texture parameter, the threshold, and
figures of merit (such as the probability of detection) a
numerically challenging and time consuming endeavour.
There is therefore a need for methods and systems which mitigate
if not avoid the drawbacks of the prior art. Preferably, these
systems and devices will avoid the use of the K-distribution
model and the use of the Gaussian distribution model.
SUMMARY OF INVENTION
The present invention provides systems and methods relating to
image processing. A sea clutter model in which the texture
random variable is drawn from a finite and discrete set of
values is used in the processing of SAR derived images. The SAR
images are divided into sub-images, with each sub-image being
processed in turn. A statistical test is then applied to each
sub-image to determine whether it contains pixels representing
only clutter or whether it contains pixels which contain non-
clutter information. The statistical test is based on the sea-
clutter model, parameters of which are derived and adapted from
each sub-image. The model is designed such that it will not
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permit more than a pre-determined number of false alarms.
Pixels in each sub-image that are determined to contain
information other than clutter are clustered, according to
proximity, into object detections. The detections from all sub-
images are combined to provide global object/vessel detection
and to group clusters that may have split across sub-image
boundaries.
In a first aspect, the present invention provides a method for
processing a radar image to detect at least one object in said
image, the method comprising:
a) receiving said radar image;
b) dividing said image into multiple sub-images;
c) processing each sub-image by:
i) estimating parameters from said sub-image for
use in calculating a texture random variable;
ii) calculating a detection threshold for said
sub-image based on said parameters estimated in
step i)
iii) for each pixel in said sub-image,
determining if said pixel contains clutter or
=
non-clutter content based on said detection
threshold;
iv) for each pixel in said sub-image, classifying
said pixel as containing clutter or non-clutter
content based on a determination in step iii);
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v) saving coordinates of each pixel containing
non-clutter content into a global set of non-
clutter pixels;
d) repeating step c) until all sub-images have been
processed;
e) processing said global set of non-clutter pixels to
result in subsets of pixels containing non-clutter
content, each subset containing pixels having non-
clutter content from a specific object, pixels in each
subset being within a predetermined proximity to one
another;
wherein said radar image is an image of a section of sea;
and
wherein said radar image is produced by a synthetic
aperture radar.
In a second aspect, the present invention provides a system for
processing radar images, the system comprising:
- an input module for receiving a radar image;
- an image divider module for dividing said radar
image into sub-images;
- a non-clutter detection module for processing sub-
images derived from said input radar image, said
detection module determining if pixels in a sub-image
contains clutter or non-clutter information;
- a clustering module for determining a location of
pixels containing non-clutter information in said sub-
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images and for creating subsets of pixels containing
non-clutter information, pixels in a subset being
within a predetermined distance from other pixels in
said subset;
wherein
- said non-clutter detection module processes each of
said sub-images by calculating a detection threshold
based on parameters from said sub-image and comparing
information from each pixel in said sub-image with
said detection threshold.
In a third aspect, the present invention provides non-transitory
computer readable media having encoded thereon computer readable
and computer executable instructions which, when executed,
implements a method for processing a radar image to detect at
least one object in said image, the method comprising:
a) receiving said radar image;
b) dividing said image into multiple sub-images;
c) processing each sub-image by:
i) estimating parameters from said sub-image for use
in calculating a texture random variable;
ii) calculating a detection threshold for said sub-
image based on said parameters estimated in step i)
iii) for each pixel in said sub-image, determining if
said pixel contains clutter or non-clutter content
based on said detection threshold;
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iv) for each pixel in said sub-image, classifying said
pixel as containing clutter or non-clutter content
based on a determination in step iii)
v) saving coordinates of each pixel containing non-
clutter content into a global set of non-clutter
pixels;
d) repeating step c) until all sub-images have been
processed;
e) processing said global set of non-clutter pixels to
result in subsets of pixels containing non-clutter content,
each subset containing pixels having non-clutter content
from a specific object, pixels in each subset being within
a predetermined proximity to one another;
wherein said radar image is an image of a section of sea;
and
wherein said radar image is produced by a synthetic
aperture radar.
BRIEF DESCRIPTION OF THE DRAWINGS
The embodiments of the present invention will now be described
by reference to the following figures, in which identical
reference numerals in different figures indicate identical
elements and in which:
FIGURE 1 is a flowchart of a method according to one
aspect of the invention;
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FIGURE 2 is a plot of the distribution function of sea
clutter comparing two ways of modeling sea clutter; and
FIGURE 3 is a block diagram of a system according to
another aspect of the invention.
DETAILED DESCRIPTION
Referring to Figure 1, a flowchart according to one aspect of
the invention is illustrated. The flowchart 10 begins at step
20 where the input image is received. The image is that of a
sea or ocean (i.e. mostly water) region and is gathered using
one or more synthetic aperure radars.
Step 30 then divides the image into multiple sub-images.
The
division of the image may be region based or content based. A
region based approach divides the image into regions and each
region becomes a sub-image. A content based approach divides
the image based on the content. Thus, as an example, a section
of the image with mostly dark pixels would form one sub-image
while a section with mostly light pixels would form another sub-
image. Another alternative divides the input image into sub-
images of a fixed size and resolution. This would split each
input image into a predetermined number of sub-images for
further processing.
In step 40, each sub-image is processed in turn. Processing
each sub-image involves applying a novel model for sea clutter.
This model, in contrast to previously used models, defines the
sea clutter texture random variable as being a number drawn
randomly from a finite and discrete set (where each element
represents a scatter type) rather than a number drawn randomly
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from a continuous, infinite set. The elements of the finite
discrete set (their values and the size of the set) as well as
the way in which these numbers are randomly selected are
estimated from the sub-image data.
As part of step 40, the parameters for each sub-image are
determined/estimated based on the contents of each sub-image.
These parameters may include the elements of the finite discrete
set, how many elements in the discrete set, and how the numbers
=
are randomly selected from within the set.
Returning to the model for sea clutter, the model defines the
texture random variable, Z, to be a discrete random variable
with a probability distribution function
>2(4 6 ((7 ¨a1) = I
/.1
where /, ci and a, are to be determined from the data
(i.e. from data within the sub-image). The ai variable
defines the set of values that the texture random
variable can assume, the ci variable defines their
probability of being selected randomly, and /, a finite
variable, defines the number of values in the set. The
statistical distribution for the clutter then becomes
II
1/i/ exp ot
2
Pea i -Fp"
(t, e1(,) tn- Wea
__________________________________________________________________ 1 Ec
2
i ____________________________________________________________ )0
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with
9
f7, aTI
Pc P77 = + a2 _4_ 0.2 = 1
(7- -- (T-
e it ("
where n denotes the humber of independent samples
averaged, called number-of-looks, 0 denotes a vector
containing all unknown parameters, and cy,2 and on2 are the
clutter and thermal noise power levels, respectively.
The a priori unknown model parameters can be estimated using the
Method-of-Moments (MoM), in which the theoretical values for the
r-th central moment
r(ii 2
ET' = ei (icai pn)r
Pr F(n)
i=l
are fitted to the measured moments trin a Least-Square
sense:
argmin
E ( 0 tr )2
E ir
A I in
r=1 m=1
Note that r does not need to be an integer value and the
number of moments R can be arbitrarily chosen but must
be larger than the total number of unknown parameters.
Once the various parameters for each sub-image have been
determined, a detection threshold using those parameters for
this sub-image is then calculated. For this, the cumulative
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distribution function (cdf) of the discrete clutter model is
utilized:
Fnt
= p, +1-171
FT (t 6) = I ¨ Eci
r(")
i=1
in which I1(.) represents the gamma function and Il(.,.)
the incomplete gamma function, respectively.
More specifically, for an operator pre-determined false
alarm rate Pfar and using the estimated model paramaters
determined above, the detection threshold 17 is computed
by numerically inverting the equation:
nr
pc a:+pn
Pfa(ii, 6) = 1 ¨ 6) = ei __________
F(ii)
i=1
The detection threshold is then applied to each pixel
within that sub-image. If the contents of that pixel
meets or exceeds the detection threshold value, then
that pixel is classified as a non-clutter pixel. If the
pixel's contents do not meet the detection threshold
value, then the pixel is considered to be a clutter
pixel. It should be clear that each sub-image may have
different parameters and, as such, each sub-image may
have different detection thresholds from other sub-
images.
Once the various sub-images have been processed, the sub-images
containing only clutter information may be discarded or be set
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aside for no further processing. The sub-images containing non-
clutter information, on the other hand, are processed further.
It should be noted that the estimated and combined clutter
parameters determined in step 40 for each sub-image may be used
to generate a clutter characterization map. Such a map would
allow for the classification of different sea surface features
such as currents, water-land boundaries, etc.
Returning to Figure 1, in step 50, the coordinates for the non-
clutter pixels are determined and these coordinates are placed
into a global set of coordinates. These coordinates identify
the locations of all non-clutter pixels in all sub-images,
taking into account the offsets of all sub-images. Non-clutter
objects imaged in the scene may have a large enough spatial
extent to cover several pixels in the radar image. These several
pixels associated with each non-clutter object are contained in
the global set in an unorganized fashion.
Once within the global set of coordinates, the non-clutter
pixels are then clustered based on each pixel's proximity to
other non-clutter pixels. The global set is processed to yield
a set of non-intersecting subsets, where each subset contains
only pixels that correspond to a single non-clutter object. The
clustering operation utilizes the fact that pixels of the same
non-clutter object should be connected to each other by
proximity. (Step 60).
Once the non-clutter pixels have been clustered together, each
group may be processed further to determine what kind of object
was captured in the image. A shape or image recognition
process may be applied to the resulting clusters of non-clutter
pixels. The shape recognition process can compare the cluster
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of sub-images to known shapes of seaborne objects such as ships,
ice bergs, whales, etc. Once a match or a close enough match is
made between the clustered sub-images and one of the known
shapes, a match may be considered to be made and that a known
object has been found in the input SAR image. If a match for a
ship or ships has been found, an alert can be sounded and a sub-
image of the area around the detected ship or ships can be
created from the input image. This created sub-image can then
be sent to another facility for either further analysis Or for
alert purposes. In addition to the dimension and shape of the
objects, more advanced radar systems, such as multi-aperture
SARs, may be used to estimate the velocity/speed of the objects
detected.
It should be noted that the above method can be implemented for
use on-board a satellite. Instead of downloading SAR images
containing extensive amount of data by way of a downlink from
the satellite to an Earth station, the satellite can perform the
automated analysis and ship detection process on the SAR image.
Detected ships and objects can then directly be reported to the
users and, if necessary, small images of those detected ships
can be downlinked as well. In addition of avoiding expensive
ground station infrastructure, this would greatly reduce the
data volume and, in turn, the latency time required to detect
and report ships in a specific region of ocean or sea.
It should also be noted that the novel model for sea clutter,
for clarity, models sea clutter as being discrete in nature and
not as a continuous texture model. This new model also accounts
for the additive thermal noise contribution and can be used to
compute the desired texture parameters and detection thresholds.
Figure 2 is a plot of the logarithm of the estimated
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distribution function of sea clutter overlaid by the optimally
fit K-distribution (red) and the new discrete texture model
(black) for sea clutter. As can be seen from Figure 2, the new
discrete texture model outperforms the K-distribution model.
Referring to Figure 3, a system for use in implementing one
aspect of the invention is illustrated. This system 200 uses an
input module 210, an image division module 220, a sub-image
pixel classification module 230, a clustering module 240, and a
shape detection module 250.
The input module 210 receives the input SAR image, either from
the SAR itself or from a data file or files. Any preprocessing
to prepare the image is performed by the input module 210.
Once the SAR image has been received, the input image is then
passed to the image division module 220. The image division
module 220 divides the input image into multiple sub-images
based on the desired implementation. As noted above, the
segmentation may be done by region, content, or sub-image size.
Other options are, of course, possible.
The divided sub-images are then passed to the pixel
classification module 230. This module checks each sub-image
and estimates the clutter model parameters within that sub-
image. The parameters are then used to calculate the non-
clutter detection threshold for that sub-image, and the
threshold is applied to each pixel within that sub-image. If a
pixel in that sub-image does not meet or exceed the threshold,
then that pixel is classified as being a clutter pixel. If the
pixel content meets or exceeds the detection threshold, then
that pixel is classified as a non-clutter pixel. This
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module would implement and apply the novel discrete texture
model for sea clutter noted above.
Once the pixels containing non-clutter information for each sub-
image have been detected, these pixels are then clustered by the
clustering module 240 based on each pixel's proximity to other
non-clutter pixels. The clustered pixels may be further
processed if necessary. Clustering may involve moving clustered
pixels together into a different area in computer memory such
that the clustered pixels are stored together and can form a
single image.
As noted above, detected ships and objects can be reported and
their image can be included in the report.
The system illustrated in Figure 3 may be implemented as being
on-board a satellite carrying a synthetic aperture radar to
provide processing capabilities once the SAR images have been
produced. Alternatively, the system may be implemented on a
ground station so that SAR images, whether received from a
satellite based SAR or an airborne SAR, can be processed to
detect seaborne objects.
The embodiments of the invention may be executed by a computer
processor or similar device programmed in the manner of method
steps, or may be executed by an electronic system which is
provided with means for executing these steps. Similarly, an
electronic memory means such as computer diskettes, CD-ROMs,
Random Access Memory (RAM), Read Only Memory (ROM) or similar
computer software storage media known in the art, may be
programmed to execute such method steps. As well, electronic
signals representing these method steps may also be transmitted
via a communication network.
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Embodiments of the invention may be implemented in any
conventional computer programming language. For example,
preferred embodiments may be implemented in a procedural
programming language (e.g."C","MATLAB") or an object-oriented
language .(e.g."C++", "java", "PHP", "PYTHON" or
Alternative embodiments of the invention may be implemented as
pre-programmed hardware elements, other related components, or
as a combination of hardware and software components.
Embodiments can be implemented as a computer program product for
use with a computer system. Such implementations may include a
series of computer instructions fixed either on a tangible
medium, such as a computer readable medium (e.g., a diskette,
CD-ROM, ROM, or fixed disk) or transmittable to a computer
system, via a modem or other interface device, such as a
communications adapter connected to a network over a medium. The
medium may be either a tangible medium (e.g., optical or
electrical communications lines) or a medium implemented with
wireless techniques (e.g., microwave, infrared or other
transmission techniques). The series of computer instructions
embodies all or part of th.,, functionality previously described
herein. Those skilled in the art should appreciate that such
computer instructions can be written in a number of programming
languages for use with many computer architectures or operating
systems. Furthermore, such instructions may be stored in any
memory device, such as semiconductor, magnetic, optical or other
memory devices, and may be transmitted using any communications
technology, such as optical, infrared, microwave, or other
transmission technologies. It is expected that such a computer
program product may be distributed as a removable medium with
accompanying printed or electronic documentation (e.g., shrink-
wrapped software), preloaded with a computer system (e.g., on
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CA 02897541 2015-07-17
Attorney Docket No. 1004P029CA01
system ROM or fixed disk), or distributed from a server over a
network (e.g., the Internet or World Wide Web). Of course, some
embodiments of the inventi n may be implemented as a combination
of both software (e.g., a computer program product) and
hardware. Still other embodiments of the invention may be
implemented as entirely hardware, or entirely software (e.g., a
computer program product).
A person understanding this invention may now conceive of
alternative structures and embodiments or variations of the
above all of which are intended to fall within the scope of the
invention as defined in the claims that follow.
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Dessin représentatif