university of british columbia september, 2007
DESCRIPTION
Applying Target Decomposition Algorithms on the Detection of Man Made Targets Using Polarimetric SAR. Flavio Wasniewski*, Ian Cumming. University of British Columbia September, 2007. Objectives. Review the Detection of Crashed Airplanes (DCA) methodology applied by Lukowski et. al . - PowerPoint PPT PresentationTRANSCRIPT
Applying Target Decomposition Algorithms on the Detection of Man Made Targets Using Polarimetric SAR
University of British ColumbiaSeptember, 2007
Flavio Wasniewski*, Ian Cumming
Objectives
1. Review the Detection of Crashed Airplanes (DCA) methodology applied by Lukowski et. al.
2. Test this methodology with a more diverse set of target clutters and types;
3. Compare its performance with available target detection algorithms;
4. Develop improvements to the methodology in order to give good detection performance to a range of target and clutter types.
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Detection of Man Made Targets with Radar Polarimetry
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High target-to-clutter ratio (not necessarily higher than in natural targets)
Dihedral scattering expected (phase information can be explored)
Polarimetric decompositions are among the most promising algorithms
Most civilian operational applications focus in ship detection
Detection of Crashed Airplanes (DCA)
Source: Lukowski et. al., CJRS, 2004
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Promising in-land application
Tested on airplanes and low vegetation clutter
Tail and wings usually remain intact and provide dihedrals
Can it be applied to all discrete man made targets? (will dihedrals always be present?)
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Methodology 1 (DCA)
The cross symbol is a logical “and” combining the 3 results.
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Methodology 2
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Methodology 3
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Methodology 4
Algorithms (1/5) – Polarimetric Whitening Filter
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Bright pixels represent strong radar returns, but targets are obscured; PWF reduces speckle (σ/µ) without affecting the resolution; Target-to-clutter ratio is improved
HH
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PWF image
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Algorithms (2/5) – Even Bounce Analysis Explores the 180° phase shift between HH and VV
Even Bounce Image
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HH
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22 hv
vvhheven S
SSE
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Algorithms (3/5) – Cameron Decomposition
Classifies the target according to the maximum symmetric component in one of six elemental scatterers.
1001
1001
0001
5.00
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i001
Target SM Z
Trihedral 1
Dihedral -1
Dipole 0
Cylinder 0.5
Narrow Diplane -0.5
Quarter Wave i
Source: Cameron, 1996
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Algorithms (4/5) – Freeman-Durden Decomposition
Decomposition of backscatter into three basic scattering mechanisms: Volume scattering: canopy scatter from a cloud of randomly oriented dipoles Double-bounce: scattering from a dihedral Surface scattering: Single bounce from a moderately rough surface
Source: Freeman et. al.
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Algorithms (5/5) – Coherence Test
Detects coherent targets based on the degree of coherence and target-to-clutter ratio.
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2*222 .4
symp
Degree of coherence
and are the Pauli components
•Closing (dilation + erosion)•Clustering•Erasing 1 and 2-pixel detections
Morphological processing
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Experiments: data sets used (1)
Gagetown dataset
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Experiments: data sets used (2)
Westham Island dataset
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Results – Target 21 (House Among Trees)
CV-580 data Target and clutter(Ikonos image)
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Results – Target 21 – Methodology 1 PWF and Even Bounce
PWF Target Map (K = 2)
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Even Bounce Target Map (K = 7 )
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Cameron, PWF and Even Bounce
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Detection Map - Methodology 1
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Results – Target 21 – Methodology 1 Cameron combined to PWF and Even Bounce
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Results – Target 21 – Methodology 2Coherence Test Target Map
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Cameron and Coherence Test Map
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Detection Map - Methodology 2
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Results – Target 21 – Methodology 3
Detection map after morphology
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Results – Target 21 – Methodology 4
Cameron + PWF + Even Bounce + Coherence Test Detection map
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Results – Target 2 (Plow)
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Results – Target 2 – Methodology 1 - Same detection results were achieved by Methodologies 2 and 4
PWF Target Map (K = 2)
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Even Bounce Target Map (K = 6 )
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Cameron, PWF and Even Bounce
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Detection Map - Methodology 1
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Results – Target 5 (Horizontal cylinders)
Cameron, PWF and Even Bounce
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Cameron and Coherence Test Map
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Man made target with no dihedral behaviour
No detections
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Results – Target 7 (House)
Detection Map - Methodology 1
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Detection Map - Methodology 2
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Detection Map - Methodology 4
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Results – Target 20 (Crashed Plane in Grass)
PWF image
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Corner reflectors
Target
Results – Target 20 - Methodology 1 - Same detection results were achieved by Methodologies 2 and 4
PWF Target Map (K = 3)
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Even Bounce Target Map (K = 5 )
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Cameron, PWF and Even Bounce
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Detection Map - Methodology 1
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Results
Methodology 1 Methodology 2 Methodology 4
Total False Alarm count 5 14 1
Total False Alarm Rate 210 1087 72
Methodology 1 Methodology 2 Methodology 4
False Alarm count
(Low Vegetation)0 3 0
False Alarm count
(High & medium Vegetation)
5 11 1
Total
Per Vegetation type
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Summary
Methodology 1 (DCA) detected the targets with no false alarms when clutter is low vegetation. It did present false alarms in high vegetation;
Methodology 2 (Coherence Test) typically detects the target with few false alarms in both situations;
Methodology 3 (Freeman-Durden decomposition) generally presented high false alarm rates in this study;
Methodology 4 (DCA + Coherence Test) performs better than DCA methodology on high vegetation clutter.
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Thank you