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SAR%20Automatic%20Target%20Recognition%20Proposal

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Title: A Model-Based Approach to the Detection and Classification of Mines in Sidescan Sonar Author: ceesr Last modified by: Administrator Created Date – PowerPoint PPT presentation

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Title: SAR%20Automatic%20Target%20Recognition%20Proposal


1
SAR Automatic Target Recognition Proposal
  • J.Bell, Y. Petillot

2
Contents
  • Background
  • ATR on SAR
  • ATR on Sonar
  • Supporting Technologies
  • Initial results on SAR
  • Way forward

3
ATR approaches
4
Unsupervised Techniques
  • Future automated systems will require all
    available information (navigation data, image
    processing models .etc.) to be fused.

5
CAD/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
6
The 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.

7
The 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)

8
Basic 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.

9
Incorporating A Priori Info
  • Object-highlight regions appear as small, dense
    clusters.
  • Most highlight regions have an accompanying
    shadow region.

Segment by minimising
10
Initial Detection Results
DETECTED OBJECT
  • Initial Results Good.
  • Model sometimes detects false alarms due to
    clutter such as the surface return requires
    more analysis!

11
Object 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.

12
The 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.

13
CSS Results
Standard Model
CSS Model
14
The 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.

15
Results
16
Results 2
17
Results 3
18
Result 4
19
BP 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.

20
Object 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
21
Modelling 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.

22
Comparing 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
23
Incorporating 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.

24
The 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.

25
Mono-view Results
  • Dempster-Shafer allocates a BELIEF to each class.
  • Unlike Bayesian or Fuzzy methods, D-S theory can
    also consider union of classes.

26
Mono-view Results
Model was tested on 66 mugshots containing
cylinders, Spheres, Truncated cones and clutter
objects.
27
Multi-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
28
Multi-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
29
Future 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.
30
Conclusions
  • 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.

31
Acknowledgements
  • We would like to thank the following institutions
    for
  • their support and for providing data
  • DRDCAtlantic, Canada
  • Saclant Centre, Italy
  • GESMA, France
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