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 • Image Based techniques • Based on large training sets • Assumes some form of linearity in the imaging process • image based only (difficult to fuse with other external data) • NN / Pattern Matching for classification • Model Based techniques • Model can be learned (trained) or imposed (CAD) • Can use physics • Can use simulation in the loop (test simulation vs data) • Can take into account non-linear image formation

4. Typical Recognition Scenario Classical Approach Imaging Platform Target Classifier Orientation Estimator

5. Target Classifier Orientation Estimator Model-Based Recognition

6. Model-Based Recognition Training Data Difficult (and weak ?) part Scene and Sensor Physics Functional Estimation Image Processing Inference

7. Our proposal REMOVE FALSE ALARM 1 2 YES Detect ROIs (MRF-based Model Saliency Context Detection) Extract Highlight/Shadow (CSS Model) False Alarm? NO Fuse Other Views Import DTM Models Classify Object Dempster-Shafer Target Positive Classification? YES NO

8. Compare real and simulated image(section 3.3.1) Segmentation Markov Trees Simulate Highlight/ Object image Shadow detection Generate set Based on parameter Context of parameters parameters extraction detection And model section(3.2) (section 3.1) (section 3.4) Models (section 3.3.2) Our proposal

9. A Markov Random Field(MRF) model framework is used. MRF models operate well on noisy images. A priori information can be easily incorporated (priors). Sonar ATR • They are used to • retrieve the underlying label field (e.g shadow/non-shadow)

10. 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.

11. Incorporating A Priori Info • Object-highlight regions appear as small, dense clusters. • Most highlight regions have an accompanying shadow region. Segment by minimising:

12. Initial Detection Results DETECTED OBJECT • Results are good (85-90% detection rate). • Model sometimes detects false alarms due to clutter such as the surface return – requires more analysis!

13. 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

14. 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.

15. CSS Results Standard Model CSS Model

16. 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

17. Results

18. Results 2

19. Results 3

20. Result 4

21. 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

22. Overview of Object Classification

23. Determining Synthetic Shadow

24. 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.

25. 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

26. 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

27. Multi-view Analysis Dempster-Shafer allows results from multiple views to be fused.

28. Multi-Image Analysis

29. Context Detection 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. Context Detection using segmentation based on Markov Random Fields Variational techniques Saliency Shape extraction for highlight / shadow Use of image formation process to force combined meaningful extraction. Active contours to perform robust extraction (statistical snakes, Mumford-Shah) The way Forward

31. Model Based classification Initial model parameters extracted from segmentation Model is refined using A simulator of the image formation + search in parameter space Direct inference using training and large databases Active Appearance models trained on large sets Robustify classification via Multi view combination Inclusion of DTM models via simulator via statistical priors? The way Forward

32. Initial tests Image Hierarchical MRF Rayleigh Segmentation Two class segmentation (Variational approach)

33. 2 complementary techniques for Sonar Simulation Pseudospectral Time Domain (PSTD) based on finite difference techniques accurate research tool computationally complex Ray Tracing combination of computer graphics ray tracing and ray solution to wave equation operational approach flexibility to incorporate approximations Simulator

34. Realistic Synthetic Data Simulated Image Tethered Object Forward Look Real Image Pipeline

35. Simulator – Synthetic Data Synthetic data used for testing algorithms 1. 2. 3. Object Detection Shadow Extraction Object Identification • Iteratively compare shadow to image generated by model • Need fast model - many approximations Markov Random Fields Cooperating Statistical Snake

36. Reduced Simulator • Only need to simulate shadows not full backscatter • Reduce complexity to increase speed • Effect on shadow minimal • Simple line of sight ray based calculation • Height field approximation • Isovelocity conditions • Co-located point source/receiver • No beampatterns or beam spreading

37. Effect of Approximations Simple line of sight simulation Complex Simulator – sphere on flat seabed Complex Simulator – sphere on rough seabeds Complex Simulator – horizontal beamwidth

38. Simulation of SAR • Amend Sonar Model • or • Amend existing SAR model e.g. MSTAR Predict-Lite • Initially require only shadow • Emphasis on computation time • Can alter membership functions used during classification to take into account non perfect simulation • Eventually extend model to generate full target signature