Ron Vainshtein
Itay Cohen
Supervisors:
Dr. Alon Amar, Yaakov Buchris
In Collaboration with: Azriel Sinai
Anomaly Detection in Multibeam Echosounder Seabed Scans
Motivation
• Multibeam seabed data.
• Anomaly detection using scans.
• Anomalies:• Mines• Pipes • Wreckage • Waste
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Multibeam Scans
• Echo sounding multibeam scanner.• SeaBat T20P by Teledyne-Reson
• 50 pings per second, 512 beams per ping
• Seabed mapping via beam echo.
• Scans affected by:• Wind
• Waves
• Vessel movement
• Depth3/35
Scan Characteristics
• Low resolution point cloud.
• Scan lines pattern.
• Spacing, direction and depth vary.
• Sparse and not sampled uniformly.
• Missing sample batches.4/35
Challenges
• Scan analysis and processing is difficult.
• Little prior information about targets.
• Small scan dataset.
• No prior work.
Anomaly detection is challenging!
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Goals
•Anomaly detection for multibeam seabed scans.
•Overcome difficulties arising from the data.
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Scan Properties in Detail
shore
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• Lines are spaced 0.2 - 0.7m.• Depends on vessel orientation.
Line Spacing in Deep Water
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Targets in Deep Water
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Missing Data
• Beams scattered away from scanner.• Scanner discards unreliable samples.
scanner
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Algorithm Stages
Holes Detection
Filling Holes
Anomaly Detection
Multibeam Scan
Regions of Interest
Holes Detection
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Delaunay Tessellation Field Estimator• Used in cosmology.
• Local density calculation.
• Adaptive to variations in density and geometry.
Holes Detection
[Schaap & Van De Weygaert ’00]
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Delaunay Triangulation
• Circumscribed circle contains only the inscribed triangle points.
• Guarantees immediate neighbors.
• Efficient O(nlog(n)).
Holes Detection
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DTFE – Density Calculation
• Take sum of areas of participating triangles.
• Density at each vertex = 1
sum.
• Linearly interpolate density.
Holes Detection
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DTFE-Based Hole Contouring
• Apply DTFE and assign density to triangles.
• Low density triangles indicate holes.
• Create connected components from triangles.
Holes Detection
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DTFE-Based Bole Contouring
• Use threshold on area to avoid small holes.
• Defines explicitly where to fix the data.
Holes Detection
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Algorithm Stages
Holes Detection
Filling Holes
Anomaly Detection
Multibeam Scan
Regions of Interest
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Filling Holes
Defining the sampling points:• Sampling grid based on the triangulation and data.
Choosing the Interpolation method:• Standard interpolation methods (linear, cubic, etc.) - poor reconstruction.
• A multiscale iterative approach is used.
Filling Holes
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Interpolation Method
Laplacian Pyramid Extension
• Pyramids – multiscale image manipulation.
• Create stack of increasing scales from surroundings.• No downsampling is used.• Each scale is created using all previous scales.
• Suitable for scattered data.
Filling Holes
[Bermanis, Coifman and Averbouch. ’13]
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Stack GenerationFilling Holes
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Filling Holes - ResultsFilling Holes
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Filling Holes – Deep WaterFilling Holes
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Algorithm Stages
Holes Detection
Filling Holes
Anomaly Detection
Multibeam Scan
Regions of Interest
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ROI Detection
>Threshold?
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ROI Detection
• Variance map is calculated with several window sizes.
• ROI decision – voting process on layers in patches.
• Patch size and threshold are parameters.
Anomaly DetectionAnomaly Detection
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Results – Target Anomaly Detection
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Results - Target
ROI 1.6m
Anomaly Detection
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Results - Background Anomaly Detection
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Results - Background
ROI 1.6m
Anomaly Detection
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Summary
• Multibeam data is challenging
• Algorithms from various fields
• New interpolation framework
• Innovative anomaly detection method
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• Will be used in new naval products.
• Fits several seabed-related applications.
• Publish a paper.
• Develop anomaly classification algorithm.
Achievements and Future Work
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Acknowledgments
• Yaakov Buchris
• Dr. Alon Amar
• SIPL staff:• Nimrod Peleg
• Yair Moshe
• Ori Bryt
• RAFAEL: Azriel Sinai
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