Four-component Scattering Power Decomposition with Rotation of Coherency Matrix

Yoshio Yamaguchi, Niigata University, JapanAkinobu SatoRyoichi Sato

Hiroyoshi YamadaWolfgang -M. Boerner, UIC, USA

Measured

decompositionrotation

Surface scattering

Double bounce

Volume scattering

Helix scattering

Scattering Power

expansion matrix

20

* 1 0

0 0 0

1*

02

0

0 0 0

130

15 5 05 7 00 0 8

14

2 0 00 1 00 0 1

130

15 - 5 0- 5 7 00 0 8

12

0 0 0

0 1 + j

0 + j 1

Four-component Scattering Power Decomposition

Y. Yajima, Y. Yamaguchi, R. Sato, H. Yamada, and W. -M. Boerner, “POLSAR image analysis of wetlands using a modified four-component scattering power decomposition,” IEEE Trans. Geoscience Remote Sensing, vol. 46, no. 6 , pp. 1667-1673, June 2008.

Measured =

N

Google earth optical image

Ps Pv

Pd

Scattering power Decomposition

© METI, JAXA

Decomposed image (Ps, Pd, Pv)

ALPSRP172780690-P1.1__A

2009/4/22

-Kyoto-Japan

34.800N135.781E

Radar line of sight

Deorientation

� TT

Concept for new decomposition

Minimization of the HV component by rotation

Ps Pv

Pd

Too much green in urban area

Azimuth slope and Oblique wallHV component

creation

Rp ( ) =1 0 00 cos 2 sin 20 – sin 2 cos 2

Rotation of Coherency Matrix

Ensemble average in window

T33 = T33 cos2 2 Re T23 sin 4 + T22 sin22

Minimization of T33 component

= 14tan- 1

2 Re T23

T22 T33

Rotation angle

Same as azimuth slope angle

Coherency matrix elements after T33 rotation

Major terms for 4-comp. decomposition

Minor terms

Unchanged

Pure imaginary : Best fit to Helix scattering

2 = 12tan- 1

2 Re T23T22 T33

measured decomposition

rotation

4-component decomposition

New decomposition scheme

Rotation of data matrix

Four-component decomposition

- 2 dB 2 dB

yes

yesno

no

Decomposed power

Four comp. Three comp. Two comp.Three comp.

if Pv < 0 , then Pc = 0 (remove helix scattering) 3 comp. (Ps, Pd, Pv) decomposition

if

Helix scattering power

Volume scattering power

Double bounce scattering

Surface scattering

Pv + Pc > TP

C = T12 ( )

10 logT11 ( ) + T22 ( ) – 2 Re T12( )

T11 ( ) + T22 ( ) + 2 Re T12( )

C0 = T11 ( ) – T22 ( ) – T33 ( ) + Pc

Ps = Pd = 0

S = T11 ( ) - 12

PvS = T11 ( ) - 12

PvS = T11 ( ) - 12

Pv

D = T22 ( ) - 730

Pv - 12

Pc D = T22 ( ) - 730

Pv - 12

Pc

C = T12 ( ) - 16

Pv

Pc = 2 Im T23 ( )

Pv = 154

T33 ( ) – 158

Pc

Pv = 154

T33 ( ) – 158

Pc

C0 > 0

Ps = S +C

2

S

Pd = D –C

2

S

Pd = D +C

2

D

Ps = S –C

2

D

TP = T11 ( ) + T22 ( ) + T33 ( )

Ps > 0 , Pd > 0

Ps > 0 , Pd < 0

Ps < 0 , Pd > 0

Ps , Pd , Pv , Pc

TP = Ps + Pd + Pv + Pc

Pv , Pc

Pd = 0

Ps = TP – Pv – Pc

Pv , Pc

Ps = 0

Pd = TP – Pv – Pc

Pv = TP – Pc

Ps = Pd = 0

Pc

D = T22 ( ) - T33 ( )

Pv = 4 T33 ( ) – 2 Pc

T = RP( ) T RP( )†=

T11 T12 T13

T21 T22 T23

T31 T32 T33

Rp ( ) =1 0 00 cos 2 sin 20 – sin 2 cos 2

= 14

tan- 1 2 Re T23

T22 T33

T =T11 T12 T13

T21 T22 T23

T31 T32 T33

= 1n k p k p

†n

C = T12 ( ) + 16

Pv

Coherency matrix rotation in imaging window

Four-component decomposition

Algorithm is given in terms of

coherency matrix elements only

Ps Pv

Pd

volume scattering

double bounce

surface scattering

Kyoto, JapanBefore After rotation

ALOS-PALSAR Quad Pol data

Patch A

Patch B

Forest area

Angle distribution

Kyoto

Power distributions

Original

Rotation

Niigata

Pine trees Oblique urban Orthogonal urban

Angle distributionNiigata

Patch E ° Patch D -20°

Angle distribution

Pi-SAR-XNiigata Japan

+4 -20

L-band Pi-SAR data: Downtown Niigata, JAPAN2007-10-04 (5*5 window)

Before After rotation

Before After T33 rotationBeijing, China

Conclusion

for fully polarimetric data sets

Four-component decomposition with

provides better classification result

Ps Pv

Pd