Automatic Target Detection in Search and Rescue ... - IEEE Xplore

Automatic Target Detection in Search and Rescue based on Yamaguchi Polarimetric Decomposition Zhang Tao1,2, Han Ping1,Wang Xiaoliang1 and Wu Renbiao1,3 1 Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, P.R.China 2 School of Electronic and Information Engineering, Tianjin University,Tianjin 300072, P.R.China 3 Defence Key Laboratory for ATR, Shenzhen University, Shenzhen 518060, P.R.China the cross-pol correlations which generally appear in complex urban area scatttering and disappear for a natural distributed scatterer. The polarimetric scattering matrix and average covariance matrix are defined as:

Abstract Based on the analysis of the polarization scattering characteristics of aircraft targets, using aircraft tail dihedral returns strong double-bounce and helix scattering signatures, a novel method of automatic target detection for crashed aircraft based on Yamaguchi polarimetric decomposition is proposed. Keywords: SAR; decomposition

Search

and

Rescue;

S

polarimetric

C 1. Introduction A SAR system for Search and Rescue operations, has the advantages of being unaffected by both weather conditions and darkness, hence it can be deployed at anytime to begin searching for crashed aircraft. In addition, the system is capable of high resolution, rapid coverage of large areas, and can detect targets under the cover of foliage, all of which contribute to dramatically improving crash site detection over conventional visual searches.

S HV º SVV »¼

2 S HH S HV

2 S HV

2

(1)

S HH SVV

2 S HV SVV

2 SVV S HV

SVV

2

º » » (2) » » » ¼

Then surface scatterer, double-bounce scatterer, volume scatterer and helix scatterer have the form

In 1997 NASA explored the use of airborne Synthetic Aperture Radar imagery to assist Search and Rescue for crashed small aircraft .they pursuited the use of polarization signatures to identify aircraft tail dihedrals in AirSAR imagery. But in their research, only so called ‘Even’ and ‘Odd’ bounce basis are concerned[1]. In this paper, Yamaguchi Polarimetric decomposition, a four component scattering model decomposition is used for crashed aircraft target polarimetric signatures analysis. The experimental results demonstrate the effectiveness of this algorithm. 2. YAMAGUCHI POLARIMETRIC DECOMPOSITION In 1998, a three-component decomposition method describes surface, double bounce, and volume scattering[2], introduced by Freeman and Durden dealing with the reflection symmetry condition that the co-pol and the cross-pol correlations are close to zero. Aiming at overcoming the disadvantages of the limitation of the co-pol and cross-pol correlations are not zero in urban area, Yamaguchi introduces a Helix scattering power as the fourth component[3]. This helix scattering term is added to take account of the co-pol and

2 ª S HH « «

« 2 S HV S HH «

« SVV S HH ¬

ª S HH «S ¬ VH

S surface

ªE «0 ¬

0º Ÿ >C @ 1 »¼

S double

ªD «0 ¬

0º Ÿ >C @ 1 »¼

Svolume

ª1 0 º «0 0 » Ÿ >C @ ¬ ¼

surface

double

volume

ªE2 « « 0 « 2 «E ¬ ªD2 « « 0 « 2 «D ¬ ª8 1 « 0 15 « «¬ 2

0 Eº » 0 0 » (3) » 0 1» ¼ 0 Dº » (4) 0 0» » 0 1» ¼ 0 2º (5) 4 0 »» 0 3 »¼

ª 1 rj 2 1 º » (6) 1« S helix 2 r j 2» «# j 2 helix 4« » #j 2 1 ¼» ¬« 1 Using the four-component approach with (3)~(6), we expand the measured covariance matrix as C f s [C ] surface  f d [C ] double  f v [C ] volume  f h [C ] helix (7) ª 1 r jº « r j 1 » Ÿ >C @ ¬ ¼

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where f s  f d  f v DQG f h are the expansion coefficients to be determined. Comparing the covariance matrix elements, we have the following five equations with six unknowns D  E  f s  f d  f v DQG f h 

f 8 2 2 2 ­ fs E  fd D  fv  h ° S HH 15 4 ° f 2 2 ° S HV fv  h ° 15 4 ° f ° 3 2 SVV fs  fd  fv  h ® 15 4 ° ° f 2

f s E  f dD  fv  h ° S HH SVV 15 4 ° f 1 °

h °¯ 2 Im( S HH S HV  S HV SVV ) 4

Figure 1 AirSAR L-Band image of Half Moon Bay The Half Moon Bay data was used for the Yamaguchi decomposition aircraft target polarimetric signatures analysis. The decomposed scattering powers Ps , Pd , Pv and Ph

(8)

corresponding to surface, double-bounce, volume, and helix contributions. Table 1 presents the results of examining the Ps , Pd , Pv , Ph , Pd / Ps and ( Pd  Ph ) / Ps ratios as

Taking equation (8) in (7), The scattering powers Ps , Pd , Pv and Ph corresponding to surface, doublebounce, volume, and helix contributions, respectively, are obtained as

potential discriminators. The table values represent the percentage of image pixels (out of 1024 x 768 image pixels) which exceed the target(Cessna, Beechcraft or Corner reflector) of interest for a particular discriminator. Table 1 Percentage of pixels polarimetric signatures Target

­ Ps f s (1  E ) ° ° Pd f d (1  D 2 ) ® Pv f v ° ° Ph f h ¯ 2

Cessna

Beechcraft

Discriminator

4. AUTOMATIC TARGET DETECTION BASED ON YAMAGUCHI POLARIMETRIC DECOMPOSITION A aircraft is partly composed of metal, and consists of regular geometric shapes such as flat plates, dihedrals, trihedrals, etc., which produce a polarization signature distinct from that of surrounding terrain and foliage. The detection techniques developed in this paper have therefore focused on the use of Yamaguchi polarimetric decomposition to identify the crashed aircraft polarimetric signatures.

reflector

Ps

95.54ˁ 26.46ˁ 0.012ˁ Double-bounce Pd 8.13ˁ 5.91ˁ 22.08ˁ Volume Pv 0.96ˁ 30.37ˁ 31.62ˁ Helix Ph 26.95% 17.26% 89.06% 0ˁ 1.89ˁ 91.31ˁ Pd / Ps 0% 6.45% 99.4% ( Pd  Ph ) / Ps Table 1 shows that the Corner reflector presents strong surface scatter and the two small craft Beechcraft and Cessna present strong double-bounce scatterer and helix scatterer. Figure 5 is Ps , Pd , Ph , Pd / Ps and ( Pd  Ph ) / Ps Basis Surface

(9)

Corner

image constructed from L-Band image of Half Moon Bay

Figure 1 presents L-Band AirSAR data(1024×768 image pixels) taken in July 1994 over Half Moon Bay, California. The resolution is 8m×6m. The Half Moon Bay area was used as a test collection site by Search and Rescue in 1994. Two intact small aircraft, a Beechcraft and a Cessna, and a corner reflector were set up at the Half Moon Bay airport.

(a) Ps Basis

(c) Ph Basis



(b) Pd Basis

(d) Pd / Ps Basis

Table 2 Percentage of pixels polarimetric signatures Crash site Target Discriminator Surface

Ps

Pd Pv Helix Ph Pd / Ps ( Pd  Ph ) / Ps

Double-bounce Volume

(e) ( Pd  Ph ) / Ps Basis Figure 2 Ps , Pd , Pv , Ph , Pd / Ps and ( Pd  Ph ) / Ps Basis images(Half Moon Bay) The experiment results of Half Moon Bay data present that aircraft target have strong double-bounce and helix scattering signatures and weak surface scattering signature. The Pd / Ps and ( Pd  Ph ) / Ps ratio as discriminator

54.94ˁ 0.21ˁ 9.72ˁ 13.73ˁ 0.09ˁ 0.51ˁ

performs best. Because the orientation between the vertical tail and the stabilizer make it possible to image a doublebounce and helix scatterer. 4. DETECTION OF CRASHED AIRCRAFT Statistics from aircraft crash sites indicate that the aircraft tail section is the part of the plane most likely to survive a crash intact. Figure 3 is an HH Basis set image constructed from AirSAR L-Band image (1024×768 image pixels) of Alaska taken in 1993. The Alaska collection represents a more characteristic environment which includes an actual crash site. The Alaska image area is a wooded region with a light canopy, which also includes a number of rock outcroppings and mountain peaks. A visual survey of the Alaska crash site, figure 3(a)(b), shows aircraft debris distributed in a wooded region. and aircraft tail section was in evidence.

(a) Pd / Ps Basis

(b) ( Pd  Ph ) / Ps Basis

Figure 4 Pd / Ps and ( Pd  Ph ) / Ps Basis image(Alaska) Figure 4 is Pd / Ps and ( Pd  Ph ) / Ps Basis image constructed from L-Band image of Alaska data. The Pd / Ps and ( Pd  Ph ) / Ps discriminator performed best (0.09% and 0.51% false alarm pixels respectively). In general most of the false alarm pixels in the Alaska image tend to cluster around the returns from the rock outcropping located along the ridge lines. 5. CONCLUSIONS The results obtained by applying both Half Moon Bay and Alaska data, the Pd / Ps DQG ( Pd  Ph ) / Ps discriminators show promise for helping to solve the automatic target detection problem for Search and Rescue. Acknowledgements

The authors would like to thank the supports of the National Science Foundation of China (No. 60979002), Table 2 presents the results of examining the Ps , Pd , Pv , Research Foundation of Civil Aviation University of China Ph , Pd / Ps and ( Pd  Ph ) / Ps ratios as potential (No. 09CAUC_E10),Open Project of Defence Key Laboratory for ATR.National University’s Basic Research discriminators. Here again, the table values represent the Foundation of China (No. ZXH2011C006), Research percentage of image pixels (out of 1024 x 768 image Studying Foundation of Civil Aviation University of China pixels)which exceed the crash site of interest for a particular (No. 2010QD03S), discriminator. Reference [1] Jackson C., Rais H., Huxtable B.. Polarimetry and Its Use in Automatic Target Detection with Examples from Search & Rescue. Proc. SPIE Vol. 3069, 1997: pp204214 Figure 3 AirSAR L-Band image of crash site in Alaska



[2]

Freeman A,Durden S L.A Three-Component Scattering Model for Polarimetric SAR Data.IEEE Transactions on Geoscience and Remote Sensing.1998,Vol. 36(3), pp963-73.

[3]



Yamaguchi Y.Takayanagi Y.Boerner W M,etc..FourComponent Scattering Model for Polarimetic SAR Image Decomposition.IEEE Transactions on Geoscience and Remote Sensing.2005,Vol. 43(8), pp1699-1706.