Si-Wei Chen, Motoyuki Sato

Tohoku University, Japan

chensw@cneas.tohoku.ac.jp

sato@cneas.tohoku.ac.jp

Model-based Polarimetric

Decomposition using PolInSAR

Coherence

2

Outline Introduction

– Current model-based decompositions

– Limitations

PolInSAR Coherence

– Estimation and optimization

Proposed Decomposition

– Adaptive volume scattering model

Comparative experiments

Conclusions

﹢ ﹢ ﹢...=C

Introduction

Polarimetric SAR (PolSAR)– Full polarimetric information

– Covariance matrix

Model-based decomposition– Better understanding the scattering mechanisms

3

2

2

2

2

2 2 2

2

HH HH HV HH VV

HH HV HV HV VV

HH VV HV VV VV

S S S S S

C S S S S S

S S S S S

(PolSARpro tutorials)

ALOS/PALSAR

Pre-event After-event

Optical Image

Decomposition

East Japan earthquake and tsunami

Ps Pv

Pd

Color-code

Double Bounce Volume Scattering Single Bounce

Model-based decomposition

Limitations

4

2

1 0

0 0 0

0

dbl dC f2

1 0

0 0 0

0

odd sC f

1 0 1 3

0 2 3 0

1 3 0 1

vol vC f

Freeman-Durden decomposition (1998)

Helix component

Nonnegative eigenvalues

Deorientation

Reflection symmetry assumption

Negative power

Scattering mechanism ambiguity

Inadaptive

(A. Freeman, Y. Yamaguchi, W.M. Boerner, J. J. Van Zyl, J.S. Lee, Y. Q. Jin, M.

Neumann, M. Arii and et al.)

General volume model

Single BounceDouble Bounce Volume Scattering

Improvements

Scattering mechanism ambiguity

General representation of the volume scattering

model

5

After

Deorientation19.48 13.52 16.75 12.79 18.81 18.53 17.56

223vP C33C 22C 224vP C 22

15

4vP CSPAN 11C

0

0 0

0

vol v

a d

C f b

d c

224vP C

22 224 15 4v vP C or P C

– For Freeman-Durden

– For Yamaguchi

Table I Averaged Backscattered Power (In dB)

221v v

a cP a b c f C

b223vP C

Decomposed volume scattering power

A

Pauli Image

Skew-oriented

building

Possible reasons and countermeasures Possible reasons

0

0 0

0

vol v

a d

C f b

d c

CountermeasuresAdaptive volume scattering model

Indirect modification of double- and single-bounce models

Balance the inputs and outputs

Utilization of both Polarimetric and

Interferometric information! 6

2

1 0

0 0 0

0

dbl dC f2

1 0

0 0 0

0

odd sC f

Double Bounce

2

22 2 HVC SOnly Volume scattering

Volume Scattering Single Bounce

Cross-

PolarizationTerrain slopes, oblique buildings

7

Outline Introduction

– Current model-based decompositions

– Limitations

PolInSAR Coherence

– Estimation and optimization

Proposed Decomposition

– Adaptive volume scattering model

Comparative experiments

Conclusions

PolInSAR coherence

Polarimetric SAR interferometry (PolInSAR)– Combination of PolSAR and InSAR

– Covariance matrix

Coherence magnitude

Optimization

8

1 12 2

1 2

1 11 1 2 22 2

ˆ( , ) ,

H

H HC C

11 12

6

12 22

H

CC

C

Polarimetric

Dependence

_1 _ 2 _ 3Opt Opt Opt1 2,

1 2

max

. . : 1s t

PolInSAR

(T. Xiong)

0 1

(K. Papathanassiou et al. )

Decorrelation sources– Signal-to-noise decorrelation

– Baseline decorrelation

– Processing decorrelation

9

sj

SNR temporal proc baseline volumee

NOTE: For manmade target

For forest

1, 1temporal volume

1, 1temporal volume

– Temporal decorrelation

– Volume decorrelation

– …

Sensitive to diverse terrains

PolInSAR coherence

Potentially, the volume scattering can be modeled from it!

Close relationship to forest structures

PolInSAR coherence:

10

PolInSAR Coherence

Optical image Optimal 1

Optimal 2 Optimal 3

11

PolInSAR Coherence

Optimal 1

Optimal 2 Optimal 3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.01

0.02

0.03

0.04

0.05

0.06 Optimal 1

Optimal 2

Optimal 3

12

Outline Introduction

– Current model-based decompositions

– Limitations

PolInSAR Coherence

– Estimation and optimization

Proposed Decomposition

– Adaptive volume scattering model

Comparative experiments

Conclusions

Proposed decomposition

Adaptive volume scattering model

13

_

_

_

_

0

0 01

0

HH HH Opt n

Opt n

vol v

Opt n

Opt n VV VV

C f

1 0 1 3

0 2 3 0

1 3 0 1

vol vC f

Use Freeman-Durden model

1 0

0 1 0

0 1

vol vC f

(A. Freeman,2007)

_

_

2,

31

Opt n

Opt n

_

2

5Opt nIf

Modeled with PolInSAR coherence

Where, is adjust to the spatial and temporal baseline parameters.

Model compatibility

14

Model Parameters

_

_

_

_

0

0 01

0

HH HH Opt n

Opt n

vol v

Opt n

Opt n VV VV

C f

– More uniform distribution

– More sensitive for diverse

terrains

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.01

0.02

0.03

0.04

0.05

0.06 Optimal 1

Optimal 2

Optimal 3

Principles for the choice of

are: _Opt n

Two unknowns:

_ ,Opt n

_ _ 3Opt n Opt

15

Model Parameters

_

_

_

_

0

0 01

0

HH HH Opt n

Opt n

vol v

Opt n

Opt n VV VV

C f

_

22 11 33

0.5

1 ,

Opt n

vol vol volC C C

Indirect modification

of double and single

bounce scattering

models!

NOTE:

_

22

_

01

Opt n

vol

Opt n

C _1 max

0 1

Opt n

0.2 0.4 0.5 0.6 0.80

0.67

1

2

3

4

0.6

0.7

0.8

0.9

= 1

Opt_n

Cv

ol2

2

1For

2 2 22 ,HV HH VVS S S

Decomposition

Flowchart

Double & single bounce

model

– Indirect modification

Adaptive decomposition

– Pixel by pixel

– .

16

PolInSAR covariance

matrix

PolSAR covariance

matrix

_, ,HH VV Opt n

6C CPO angle

C Deorientation C

1 0 1 3

0 2 3 0

1 3 0 1

vol vC f

_

_

_

_

0

0 01

0

HH HH Opt n

Opt n

vol v

Opt n

Opt n VV VV

C f

Non-negative eigenvalues constraintremaider VolC C C

13Re 0C

1

13Re 0C

1

, ,d sf f , ,d sf f

,d sP P ,d sP P

, ,v d sP P P

v d sSPAN P P P

Yes No

Yes No

Double dominant Odd dominant

11 22 33SPAN C C C

_ 2 5Opt n

13Re 0C

vf

22vP C

2

22 2 HVC S

Volume scattering

Double bounce

Single bounce

17

Outline Introduction

– Current model-based decompositions

– Limitations

PolInSAR Coherence

– Estimation and optimization

Proposed Decomposition

– Adaptive volume scattering model

Comparative experiments

Conclusions

18

Experiment-I

E-SAR PolInSAR data

– Test site: Oberpfaffenhofen, Germany

– L-band

– Data size : 1300×1200

Azi

mu

th

Range

I

II

Optical image PolInSAR coherence RGB image

HH, HV, VV

Master track Pauli image

HH-VV, HV, HH+VV

E-SAR(PolSARpro tutorials)

19

Decomposition _ After deorientation

Full scene

Freeman-Durden Yamaguchi Proposed

Forest region

Freeman-Durden Yamaguchi Proposed

Ps Pv

Pd

Color-code

20

Skew-oriented built-up region

Freeman-Durden Yamaguchi Proposed

21

E-SAR PolInSAR dataBioSAR-2008 campaign L band Repeat-pass dataset

Spatial Baseline: 30 m Temporal baseline: 110min

Data size : 1496×840

Pauli Image Coherence RGB Image

Experiment - II

Optical Image

Mar. 2008 Jan. 2009 Oct. 2008

Azi

mu

th

Range

VV, HV, HHHH-VV, HV, HH+VV

Logged after the BioSAR 2008

22

Decomposition _ After deorientation

Freeman-Durden Yamaguchi ProposedOptical Image

More sensitive and better fit for diverse forest terrains!

Ps Pv

Pd

Color-code

23

Outline Introduction

– Current model-based decompositions

– Limitations

PolInSAR Coherence

– Estimation and optimization

Proposed Decomposition

– Adaptive volume scattering model

Comparative experiments

Conclusions

24

Conclusions

Adaptive volume scattering model

– Using PolInSAR coherence

– Better fit for different terrains

– Indirect modification of double- and single-bounce

scattering models

Adaptive decomposition

– Fully usage of the information

– Successfully discriminate the skew-oriented

buildings as manmade structures

– Overcome the scattering mechanism ambiguity

– Sensitive to diverse forest terrains

26

General representation of the volume scattering

model

Limitation of current model

Before

Deorientation19.48 14.10 15.26 14.69 20.71 20.43 19.47

After

Deorientation19.48 13.52 16.75 12.79 18.81 18.53 17.56

223vP C33C 22C 224vP C 22

15

4vP CSPAN 11C

0

0 0

0

vol v

a d

C f b

d c

224vP C

22 224 15 4v vP C or P C

– For Freeman-Durden

– For Yamaguchi

Table I Averaged Backscattering Power (In dB)

221v v

a cP a b c f C

b223vP C

Decomposed volume scattering power

A

Pauli Image

Skew-oriented

building

27

Optical image HH-HH

VV-VV HV-HV

PolInSAR Coherence

28

HH-HH

VV-VV HV-HV

PolInSAR Coherence

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.01

0.02

0.03

0.04

0.05

0.06

VV-VV

HV-HV

HH-HH

0.2 0.4 0.5 0.6 0.80

0.67

1

2

3

4

0.6

0.7

0.8

0.9

= 1

Opt_n

Cv

ol2

2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.05

0.1 Forest Region

Built-up Region

29

Model Parameters

Optimal 3 CoherenceOptical Image

_

_

_

_

0

0 01

0

HH HH Opt n

Opt n

vol v

Opt n

Opt n VV VV

C f

_

22

_

01

Opt n

vol

Opt n

C _1 max

0 1

Opt n

30

Skew-oriented built-up region

Freeman-Durden Yamaguchi Proposed

MethodBuilt-up area Forest area

Freeman-Durden 20 32 48 9 88 3

Yamaguchi 22 25 53 7 82 11

Proposed 29 8 63 13 81 6

sPdP vP sPdP vP

Table II Scattering Power Contribution (%)

Scattering power

31

Skew-oriented built-up region

Freeman-Durden Yamaguchi Proposed

32

Skew-oriented built-up region

Freeman-Durden Yamaguchi Proposed

33

Skew-oriented built-up region

Freeman-Durden Yamaguchi Proposed

Optimal 1

PolInSAR Coherence

Optimal 2 Optimal 3

Optimal 1

PolInSAR Coherence

Optimal 2 Optimal 3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.01

0.02

0.03

0.04

0.05

Optimal 1

Optimal 2

Optimal 3

Optical Image Freeman-Durden Yamaguchi Proposed

Decomposition _ Volume scattering contribution

37

ALOS/PALSAR datasetsALOS/PALSAR

2007-4-02 2007-05-18

Pauli ImageOptical Image

Azi

mu

th

Range

Spatial baseline: 299m

Temporal baseline: 46 days

38

PolInSAR coherence _ H-V

HH-HH VV-VV HV-HV

39

PolInSAR coherence _ Optimal

Opt 1 Opt 2 Opt 3

40

PolInSAR coherence _ Histogram

41

Decomposition _ After deorientation

Freeman-Durden Yamaguchi Proposed

Ps Pv

Pd

Color-code

42

Built-up region - I

Optical image Freeman-Durden

ProposedYamaguchi

Ps Pv

Pd

Color-code

43

Built-up region - II

Optical image Freeman-Durden

ProposedYamaguchi

Ps Pv

Pd

Color-code