TH2.L09.4 - CHARACTERISTIC ANALYSIS OF VEHICLE TARGET IN QUAD-POL RADARSAT-2 SAR IMAGES
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Transcript of TH2.L09.4 - CHARACTERISTIC ANALYSIS OF VEHICLE TARGET IN QUAD-POL RADARSAT-2 SAR IMAGES
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CHARACTERISTIC ANALYSIS OF VEHICLE TARGET IN QUAD-POL RADARSAT-2 SAR IMAGES
Bo ZHANG, Hong ZHANG, Chao [email protected]
Center for Earth Observation and Digital Earth Chinese Academy of Sciences
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Outline
• Motivation
• Experiment data sets
• Truck characteristic analysis methods
• Experiment results
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Motivation• High resolution SAR image brings to new application
[1] G. Palubinskas, H. Runge. Radar Signatures of a Passenger Car. IEEE TRSL. 2007, 4( 4):644-648[2] Jean Beaudin, RADARSAT-2 GMTI Project. http://www.ottawa.drdc-rddc.gc.ca/html/RADARSAT_2_GMTI-eng.html[3] Tom I. Lukowski, Bing Yue, and Karim Mattar. Synthetic Aperture Radar for Search and Rescue:Polarimetry and Interferometry. IGARSS, Anchorage, AK, Sep. 2004, vol. 4, pp. 2479- 2482.
TerraSAR-X COSMO-SkyMed Radarsat-21m X-bandHigh resolution 3 m C-band1m X-band
Quad polarization 8m
Dual-receive-antennaSpecial feature
Technical possible ×
New application: Traffic monitoring application
Airplane detection & recognition
Vessel detection & recognition
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Motivation
• Object characteristic analysis is important for monitoring system– object detection, discrimination and identification.
SAR image Target detection Target discrimination Target identification
Target character vector
Target
Target character vector
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MotivationThe objective of this researchTraffic monitoring using quad polarimetric Radarsat-2 SAR image under the condition of disrupted traffic states caused by factors like snow disaster
In this application, vehicles were eager to be slow or static
Quad-PolSAR image
VehicleRCS analysisRoad network
GPS
Vehicle extraction
Image polarization
Image incidence angle
Image polarization
Background material
Background roughness
Target aspect angle
Target model
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Experiment data sets• Radarsat-2 Data sets
Image parameters Experiment 1 Experiment 2Image time March 2009 Sep 2009Image mode Fine Quad-Pol Fine Quad-PolData format SLC complex SLC complexPixel Space (m) 4.93*4.73 4.74*4.73Incidence angle (degree) 22.16~24.09 38.37~ 39.85Orbit Ascend DescendFrequency(GHz) 5.4 5.4Look number 1 1Range processing bandwidth(MHz)
30.0 30.0Azimuth processing bandwidth(Hz)
855 855
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Experiment data sets
• Experiment 1 • Experiment 2
Road background
Material Mixed soil Asphalt
Width 13 24
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Experiment Trucks• Experiment 1 • Experiment 2
TruckSize(L, W, H) 8.7, 2.3, 3.4 11.9, 2.5,3.8
Rain cloth Half cover Close cover
SARSAR
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Characteristic analysis methods
• Vehicle azimuth measure• Vehicle RCS measure• Vehicle polarization component measure
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Vehicle azimuth measure
GPSGeological compass
Z
SAR flightdirection (X) x
y
Geographic north
Magnetic north
Aspect angle
incidenceaangle
SAR strip
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Vehicle RCS measure
( ) ( )∫ ∫ ∑∞
∞−
∞
∞−Δ⋅Δ≈=
tctcyxdxdyyx
, 00 ,, σσσ
SAR image radiometric correction
σ0 SAR image
Quad-Pol SLC image
Truck imageChip (c,t)
Segmention with GPS coordination
Radarsat -2 metadata
△x × △y : represents the area of one pixel in SAR image
( )∑ tctc
, 0 ,σ : stands for the integral of surface targets at the position of (c,t)
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Polarization component measure
• Pauli decomposition
– When the material meet the reciprocity condition
• SPAN
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Experiment results
• Difference in incidence angle• Difference in azimuth angle • Difference at configuration of truck
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Difference in incidence angle
SAR
H
H
H
• Experiment 1• Incidence angle 23.2 °
SAR
H
H H
Fig 3(a). Experiment 1 σ0 of 0 °, 225° and 270°Fig 3(b): Experiment 2 σ0 of 180°, 225° and 270°
• Experiment 2• Incidence angle 39.7 °
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Difference in azimuth angle
Figure 4: Target slice of Pauli decomposition and its composition ratio at Experiment 1(left) and Experiment 2 (right)
• Experiment 1 • Experiment 2SAR
HH
H
Az: 0 ° Az: 225° Az: 270°
SARH
H H
Az: 180 ° Az: 225° Az: 270°
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Difference at configuration • Experiment 1 • Experiment 2
270 °span image
270 °span image
270 °pauli components 270 °pauli components
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Discussion & conclusion
• The impact placed by incident angle on the backscattering characteristics of vehicle target is the most significant.
• polarization decomposition technique is benefit for further verify the characteristics of vehicle target.
– Double bounce is the most important characters to trucks on road.
– Parallel to SAR flight direction is the best azimuth for vehicle detection
• Considering the priori geographic information constraint, detection and extraction of vehicle target and vehicle cluster can be achieved under the guidance of simple background as road
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