SAR Automatic Target Recognition Proposal

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

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

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 MLO’s (MRF-based Model) Extract Highlight/Shadow (CSS Model) False Alarm? NO Fuse Other Views Classify Object (Dempster-Shafer) MINE Positive Classification? 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. A Markov Random Field(MRF) model framework is used. MRF models operate well on noisy images. A priori information can be easily incorporated. The Detection Model • They are used to • retrieve the underlying label field (e.g shadow/non-shadow)

8. Basic 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.

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. 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. Object Feature Extraction

12. 2 Statistical Snakes segment the mugshot image into 3 regions : object-highlight, object-shadow and background. The CSS Model • 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.

13. CSS Results Standard Model CSS Model

14. 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. The Combined Model

15. Results

16. Results 2

17. Results 3

18. Result 4

19. 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%. BP ’02 Results

20. 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. Object Classification 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. 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. Comparing the Shadows HAUSDORFF DISTANCE

23. 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. Incorporating Knowledge

24. 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. The Classification Decision

25. Dempster-Shafer allocates a BELIEF to each class. Unlike Bayesian or Fuzzy methods, D-S theory can also consider union of classes. Bel(cyl)=0.42 Bel(sph)=0.0 Bel(cone)=0.0 Bel(clutter)=0.46 Bel(cyl)=0.83 Bel(sph)=0.0 Bel(cone)=0.0 Bel(clutter)=0.08 Bel(cyl)=0.0 Bel(sph)=0.303 Bel(cone)=0.45 Bel(clutter)=0.045 Mono-view Results

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.

28. Multi-Image Analysis

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. 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. Conclusions

31. We would like to thank the following institutions for their support and for providing data: DRDC–Atlantic, Canada Saclant Centre, Italy GESMA, France Acknowledgements