Automated Detection and Classification Automated Detection and Classification ModelsModels

SAR Automatic Target Recognition SAR Automatic Target Recognition ProposalProposal

J.Bell, Y. PetillotJ.Bell, Y. Petillot

Automated Detection and Classification Automated Detection and Classification ModelsModels

ContentsContents

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

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ATR approachesATR approaches

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Unsupervised Techniques Unsupervised Techniques

• Future automated systems will require all available information (navigation data, image processing models .etc.) to be fused.

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CAD/CAC ProposalCAD/CAC Proposal

Detect MLO’s(MRF-based Model)

Fuse Other Views

ExtractHighlight/Shadow

(CSS Model)

Classify Object(Dempster-Shafer)

FalseAlarm?

PositiveClassification?

1 2 YES

YES

NO

NOMINE

REMOVE FALSE ALARM

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The Sonar ProcessThe 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.

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The Detection ModelThe 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 toretrieve the underlying label field (e.g shadow/non-shadow)

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Basic MRF TheoryBasic MRF Theory

A pixel’s class is determined by 2 terms:

– The probability of being drawn from each classes distribution.

– The classes of its neighbouring pixels.

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Incorporating A Priori InfoIncorporating A Priori Info

• Object-highlight regions appear as small, dense clusters.

• Most highlight regions have an accompanying shadow region.

),()(ln),()],(1[),(),,(,

2

Ss ts Ss Ss

sssXstsstsss oxsexxxyxoyxU Segment by minimising:

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Initial Detection ResultsInitial Detection Results

• Initial Results Good.• Model sometimes detects false alarms due to clutter

such as the surface return – requires more analysis!

DETECTED OBJECT

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Object Feature ExtractionObject Feature Extraction

• The object’s shadow is often extracted for classification.

• The shadow region is generally more reliable than the object’s highlight region for classification.

• Most shadow extraction models operate well on flat seafloors but give poor results on complex seafloors.

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The CSS ModelThe 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 object’s shadow region can only be as wide as its highlight region.

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CSS ResultsCSS ResultsCSS ModelStandard Model

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The Combined ModelThe 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.

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ResultsResults

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Results 2

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Results 3

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Result 4

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BP ’02 ResultsBP ’02 Results

• The combined detection/CSS model was run on 200 BP’02 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%.

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Object ClassificationObject Classification• The extracted object’s 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 objectShadows from the same object

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Modelling the Sonar ProcessModelling 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.

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Comparing the ShadowsComparing 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.

HAUSDORFFDISTANCE

),(),,(max),( ABhBAhBAH

||||min max),( babahBbAa

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Incorporating KnowledgeIncorporating 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.

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parjj

hausjjj

finalj HH

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The Classification DecisionThe 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.

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Mono-view ResultsMono-view Results

• Dempster-Shafer allocates a BELIEF to each class.

• Unlike Bayesian or Fuzzy methods, D-S theory can also consider union of classes.

Bel(cyl)=0.83Bel(sph)=0.0Bel(cone)=0.0Bel(clutter)=0.08

Bel(cyl)=0.0Bel(sph)=0.303Bel(cone)=0.45Bel(clutter)=0.045

Bel(cyl)=0.42Bel(sph)=0.0Bel(cone)=0.0Bel(clutter)=0.46

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Mono-view ResultsMono-view ResultsModel was tested on 66 mugshots containing cylinders,Spheres, Truncated cones and clutter objects.

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Multi-view AnalysisMulti-view AnalysisDempster-Shafer allows results from multiple views to be fused.

Mono-Image Belief Fused BeliefObj Cyl Sph Cone Clutt Objs

FusedCyl 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

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Multi-Image AnalysisMulti-Image Analysis

Mono-Image Belief Fused BeliefObj Cyl Sph Cone Clutt Objs

FusedCyl 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

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Future ResearchFuture ResearchThe current detection model considers objects as a Highlight/Shadow pair. An object can also be considered as a discrepancy in the surrounding texture field.

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ConclusionsConclusions

• 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.

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Acknowledgements

We would like to thank the following institutions for

their support and for providing data:

DRDC–Atlantic, Canada

Saclant Centre, Italy

GESMA, France