CHARACTERISTIC ANALYSIS OF VEHICLE TARGET IN QUAD-POL RADARSAT-2 SAR IMAGES

Bo ZHANG, Hong ZHANG, Chao WANGbozhang@ceode.ac.cn

Center for Earth Observation and Digital Earth Chinese Academy of Sciences

Outline

• Motivation

• Experiment data sets

• Truck characteristic analysis methods

• Experiment results

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

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

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

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

Experiment data sets

• Experiment 1 • Experiment 2

Road background

Material Mixed soil Asphalt

Width 13 24

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

Characteristic analysis methods

• Vehicle azimuth measure• Vehicle RCS measure• Vehicle polarization component measure

Vehicle azimuth measure

GPSGeological compass

Z

SAR flightdirection (X) x

y

Geographic north

Magnetic north

Aspect angle

incidenceaangle

SAR strip

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)

Polarization component measure

• Pauli decomposition

– When the material meet the reciprocity condition

• SPAN

Experiment results

• Difference in incidence angle• Difference in azimuth angle • Difference at configuration of truck

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 °

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°

Difference at configuration • Experiment 1 • Experiment 2

270 °span image

270 °span image

270 °pauli components 270 °pauli components

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