Title: SAR%20Automatic%20Target%20Recognition%20Proposal
1SAR Automatic Target Recognition Proposal
2Contents
- Background
- ATR on SAR
- ATR on Sonar
- Supporting Technologies
- Initial results on SAR
- Way forward
3ATR approaches
4Unsupervised Techniques
- Future automated systems will require all
available information (navigation data, image
processing models .etc.) to be fused.
5CAD/CAC Proposal
REMOVE FALSE ALARM
1
2
YES
Detect MLOs (MRF-based Model)
Extract Highlight/Shadow (CSS Model)
False Alarm?
NO
Fuse Other Views
Classify Object (Dempster-Shafer)
Positive Classification?
MINE
YES
NO
6The Sonar Process
- Sonar images represent the time of flight of the
sound rather than distance. - Objects appear as a highlight/shadow pair in the
sonar image.
7The Detection Model
- A Markov Random Field(MRF) model framework is
used. - MRF models operate well on noisy images.
- A priori information can be easily incorporated.
- They are used to
- retrieve the underlying label field (e.g
shadow/non-shadow)
8Basic MRF Theory
- A pixels class is determined by 2 terms
- The probability of being drawn from each classes
distribution. - The classes of its neighbouring pixels.
9Incorporating A Priori Info
- Object-highlight regions appear as small, dense
clusters. - Most highlight regions have an accompanying
shadow region.
Segment by minimising
10Initial Detection Results
DETECTED OBJECT
- Initial Results Good.
- Model sometimes detects false alarms due to
clutter such as the surface return requires
more analysis!
11Object Feature Extraction
- The objects shadow is often extracted for
classification. - The shadow region is generally more reliable than
the objects highlight region for classification. - Most shadow extraction models operate well on
flat seafloors but give poor results on complex
seafloors.
12The CSS Model
- 2 Statistical Snakes segment the mugshot image
into 3 regions object-highlight, object-shadow
and background.
- A priori information is modelled
- The highlight is brighter than the shadow
- An objects shadow region can only be as wide as
its highlight region.
13CSS Results
Standard Model
CSS Model
14The Combined Model
- Objects detected by MRF model are put through the
CSS model. - The CSS snakes are initialised using the label
field from the detection result. This ensures a
confident initialisation each time. - The CSS can detect MANY of the false alarms.
False alarms without 3 distinct regions ensure
the snakes rapidly expand, identifying the
detection as a false alarm. - Navigation info is also used to produce height
information which can also remove false alarms.
15Results
16Results 2
17Results 3
18Result 4
19BP 02 Results
- The combined detection/CSS model was run on 200
BP02 data files containing 70 objects. - 80 of the objects where detected and features
extracted(for classification). - 0.275 false alarms per image.
- The surface return resulted in some of the
objects not being detected. Dealing with this
would produce a detection rate of 91.
20Object Classification
- The extracted objects shadow can be used for
classification. - We extend the classic mine/not-mine
classification to provide shape and dimension
information. - The non-linear nature of the shadow-forming
process ensures finding relevant invariant
features is difficult.
Shadows from the same object
21Modelling the Sonar Process
- Mines can be approximated as simple shapes
cylinders, spheres and truncated cones. - Using Nav data to slant-range correct, we can
generate synthetic shadows under the same sonar
conditions as the object was detected. - Simple line-of-sight sonar simulator. Very fast.
22Comparing the Shadows
- Iterative Technique is required to find best fit.
Parameter space limited by considering highlight
and shadow length. - Synthetic and real shadow compared using the
Hausdorff Distance. - It measures the mismatch of the 2 shapes.
HAUSDORFF DISTANCE
23Incorporating Knowledge
- As the technique is model-based, information on
likely mine dimensions can be incorporated. - Limited information from the highlight region can
also be used to distinguish between the tested
classes. - We obtain an overall membership function for each
class.
24The Classification Decision
- A decision could be made by simply defining a
Positive Classification Threshold. This is a
hard decision and non-changeable. - The lawnmower nature of Sidescan surveys
ensures the same object is often viewed multiple
times. The model should ideally be capable of
multi-view classification. - We use DEMPSTER-SHAFER theory.
25Mono-view Results
- Dempster-Shafer allocates a BELIEF to each class.
- Unlike Bayesian or Fuzzy methods, D-S theory can
also consider union of classes.
26Mono-view Results
Model was tested on 66 mugshots containing
cylinders, Spheres, Truncated cones and clutter
objects.
27Multi-view Analysis
Dempster-Shafer allows results from multiple
views to be fused.
Mono-Image Belief Mono-Image Belief Mono-Image Belief Mono-Image Belief Mono-Image Belief Fused Belief Fused Belief Fused Belief Fused Belief Fused Belief
Obj Cyl Sph Cone Clutt Objs Fused Cyl Sph Cone Clutt
1 0.70 0.00 0.00 0.21 1 0.70 0.00 0.00 0.21
2 0.83 0.00 0.00 0.08 1,2 0.93 0.00 0.00 0.05
3 0.83 0.00 0.00 0.08 1,2,3 0.98 0.00 0.00 0.01
4 0.17 0.00 0.00 0.67 1,2,3,4 0.96 0.00 0.00 0.03
28Multi-Image Analysis
Mono-Image Belief Mono-Image Belief Mono-Image Belief Mono-Image Belief Mono-Image Belief Fused Belief Fused Belief Fused Belief Fused Belief Fused Belief
Obj Cyl Sph Cone Clutt Objs Fused Cyl Sph Cone Clutt
5 0.00 0.17 0.23 0.45 5 0.00 0.17 0.23 0.45
6 0.00 0.00 0.37 0.44 5,6 0.00 0.00 0.30 0.60
7 0.00 0.303 0.45 0.045 5,6,7 0.00 0.02 0.67 0.17
8 0.00 0.32 0.23 0.31 5,6,7,8 0.00 0.01 0.62 0.20
29Future Research
The current detection model considers objects as
a Highlight/Shadow pair. An object can also be
considered as a discrepancy in the surrounding
texture field.
30Conclusions
- Automated Detection/Feature Extraction model has
been developed and tested on a large amount of
data. Good Results obtained, improvements
expected when surface returns removed. - Classification model uses a simple sonar
simulator and Dempster-Shafer theory to classify
the objects. Extends mine/not-mine
classification to provide shape and size
information. - Future research is focusing on texture
segmentation to complement the current work.
31Acknowledgements
- We would like to thank the following institutions
for - their support and for providing data
-
- DRDCAtlantic, Canada
-
- Saclant Centre, Italy
-
- GESMA, France