exploitation of general polarimetric model-based...

PolInSAR 2017 | 23-27 January | ESA-ESRIN

Exploitation of General Polarimetric Model-based

Decomposition by Incorporating A Generalized

Volume Scattering Model

Qinghua Xie1, 2 ([email protected]), J. David Ballester-Berman2,

Juan M. Lopez-Sanchez2, Jianjun Zhu1, Changcheng Wang1

1. School of Geosciences and Info-Physics, Central South University, Changsha, China

2. Institute for Computing Research (IUII), University of Alicante, Alicante, Spain


4, Conclusion

3, Simulation And Experimental Result

1, Research Motivation

2, General Decomposition Models & Algorithm

Outline


Model-Based Decomposition Methods

* * * *data s surface d dihedral v volume c helixT f T f T f T f T (+…)

FD3: Freeman-Durden 3-Component

Decomposition (Freeman, TGRS, 1998)

Y4O: Yamaguchi 4-Component Decomposition

(Yamaguchi, TGRS, 2005)

Y4R: Y4 with Rotation (Yamaguchi , TGRS,

2011)

S4R: Y4R+ Volume Scattering by Dihedral

(Sato, GRSL, 2012)

G4U: S4R+ Additional Unitary Transformation

Which Makes T23=0 (Singh, TGRS, 2013)

……

By Exploiting key ideas in previous methods

Chen: 4 Tv Models + 2 Rotation Angles + Directly

Parameters Solving (Chen, TGRS, 2014)

Xie: Chen’s decomposition framework +modified

parameters inversion algorithm (Xie, RS, 2016)

Question : Just specific volume scattering models. Whether more generalized volume

scattering models can help improving general model-based decomposition (GMD)?

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4, Conclusion




Outline


2 0 01

= 0 1 04

0 0 1

vol random vT f

1 0 01

= 0 1 03

0 0 1

vol entropy vT f

15 5 01

= 5 7 030

0 0 8

vol horizontal vT f

15 5 01

= 5 7 030

0 0 8

vol vertical vT f

v s S d D c residualT T T T T T

*

2

1 0

= 0

0 0 0

s sT f

General Decomposition Models

Chen et.al, TGRS, 2014

0 0 01

= 0 12

0 1

c cT f j

j

2

*

d

0

= 1 0

0 0 0

dT f

* *

2 22

3 3

2 2 2

1 cos sin 2

1= cos cos sin 4

2

1sin 2 sin 4 sin

2

S S

H

s S S s S S S S S

S S S

T R T R f

Volume

Scattering

Surface

Scattering

Dihedral

Scattering

Helix

Scattering

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2

* 2

3 3

* 2

cos2 sin 2

1cos2 cos 2 sin 4

2

1sin 2 sin 4 sin 2

2

D D

H

d d d d d d D D D

D D D

T R T R f


Parameters Inversion Algorithm

v s S d D c residualT T T T T T Inversion Model

9 Real Observations 11 22 33 12 12 13 13 23 23{ , , , Re( ), Im( ), Re( ), Im( ), Re( ), Im( )}T T T T T T T T T

9 Unknown Parameters , , , , Re( ), Im( ), , ,v s d c S Df f f f

Optimization Criterion 2

2min : residualT

230 , , 0 2 Im

, , 14 4

v s d c

S D

f f f SPAN f TBoundary Conditions

Initial Values Traditional Yamaguchi-based decomposition methods, e.g.Y4O, Y4R

Nonlinear Optimization to Solve 9 Dimensional Problem

/26


Modified Chen Method

Quantitative Analysis of Polarimetric Model-Based Decomposition Methods

PolSAR Data

Monte Carlo

Simulation Test

Real data Test

Model-based Decomposition

Traditional Methods 1) Y4O 2) Y4R 3) S4R 4) G4U 5) Chen

Modified Chen Method 1) Chen’s Decomposition framework 2) Modification of The Inversion Algorithm Redefined boundary conditions; Variable transformation; Modification for Initial Values

1) Quantitative analysis of the whole parameter set; 2) Model parameters within physically valid ranges

Xie, Q.; Ballester-Berman, J.D.; Lopez-Sanchez, J.M.; Zhu,

J.; Wang, C. Quantitative Analysis of Polarimetric Model-

Based Decomposition Methods. Remote Sens. 2016, 8, 977.

1

1=

nH

simC uun

/26


Volume Scattering Model: GVSM

0 / 3

1 10 / 3 0

23 / 2(1 ) / 3

/ 3 0 1

VC

2 2

hh vvS S

According to the expression of GVSM in case of setting the value of shape parameter as 1/3

(i.e., dipoles) for the covariance matrix

Unitary transformation

1 10

2 3 2

1 1 10

2 2 33(1 )

2 3 10 0

2 3

GVSM

vT

GVSM (Antropov et al. TGRS, 2011)

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Show the Generality of GVSM

Radar vegetation index ( Kim & Van zyl)

1 2 3

1 2 3

4min , ,RVI

11

logN

i

i N i i Ni

ij

H P P P

Polarimetric scattering entropy (Cloude & Pottier)

-10 -5 0 5 100.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(dB)

RV

I an

d H

RVI

H

/26


Volume Scattering Model: SAVSM

SAVSM (Huang et al. TGRS, 2016) ),

1

sin2

vp

1

cos2

hp

1

2rp

2 0 01

= 0 1 04

0 0 1

vol randomT

15 5 01

= 5 7 030

0 0 8

vol horizontalT

15 5 01

= 5 7 030

0 0 8

vol verticalT

1 0

0 0h dipoleS

1 0

0 0h dipoleS

Freeman&Yamaguchi models

V SAVSM

VT n

H SAVSM

VT n

n H-SAVSM V-SAVSM

0

1

Freeman&Yamaguchi VS SAVSM models

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Generality of SAVSM

0 5 10 15 20 25-25

-20

-15

-10

-5

0

5

10

15

20

25

n

(d

B)

H-SAVSM

V-SAVSM

0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

nR

VI

an

d H

RVI

H

/26


2 0 01

= 0 1 04

0 0 1

vol random vT f

1 0 01

= 0 1 03

0 0 1

vol entropy vT f

15 5 01

= 5 7 030

0 0 8

vol horizontal vT f

15 5 01

= 5 7 030

0 0 8

vol vertical vT f

v s S d D c residualT T T T T T

GMD with Generalized Volume Models

Chen et.al, TGRS, 2014

Volume

Scattering

G

v v s s S d d D c c residualT f T f T f T f T T

G

vT

Original

vT

Proposed

Parameter Inversion Same as Modified Chen method(Xie et.al, Remote Sensing, 2016)

GVSM

vT

SAVSM

vT n“continuous” volume models

“discrete” volume models

(Huang et al. TGRS, 2016).

(Antropov et al. TGRS, 2011 )

/26

GMD-GVSM

GMD-SAVSM


Coherency Matrix Rotated Coherency Matrix

Ratio Estimation

GVSM

v v s s S d d D c c residualT f T f T f T f T T

Modified Parameters Inversion Algorithm

Solution

, , , , , , ,v s d c S Df f f f

2 2, 1 , 1 ,v v s s d d c cP f P f P f P f

T 'T

2 2

10=10log hh vvS S

Flowchart of “GMD-GVSM” method

/26


Flowchart of GMD-SAVSM

Coherency Matrix

Ratio Estimation

SAVSM

v v opt s s S d d D c c residualT f T n f T f T f T T

2 2

10=10log hh vvS S

Modified Parameters Inversion Algorithm

Solution

, , , , , , ,v s d c S Df f f f

2 2, 1 , 1 ,v v s s d d c cP f P f P f P f

T

0dB H-SAVSMYesV-SAVSM No

Loop from

No

Yes

First solution ,then second loop from

1 10.9 : 0.1: 0.9st stn n n

0 :1: 20n

min T SAVSMRVI RVI n

min T SAVSMRVI RVI n

1stn

No

Optimum

Yes

optn

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4, Conclusion




Outline


Monte Carlo Simulation Tests

Parameter Quantity Value

vf volume scattering coefficient 0:2:10

sf surface scattering coefficient 0:2:10

df dihedral scattering coefficient 0:2:10

cf helix scattering coefficient 0.01

s orientation angle in surface scattering model -10°

d orientation angle in dihedral scattering model -15° ratio parameter in dihedral scattering model 0.3515 - 0.0768i ratio parameter in surface scattering model -0.3377 incidence angle 45° differential propagation phase 10°

s soil dielectric constant 10

t trunk dielectric constant 30

1

Table 1. Values for input parameters

we simulated 216 different cases. In every case, we performed 1000 realizations

and 15×15 looks were averaged for reducing speckle noise

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Monte-Carlo Simulation for PolSAR Data

(1) Eigenvalue decomposition for the given covariance matrix C and computation of its square root:

(2) Simulate a normal complex Gaussian random vector using Gaussian random number generators

(4) Compute the n-looks averaged covariance matrix

(3) Compute the simulated single look complex vector

1/2u C v

1/2

V CV=D

C V*

H

D

*

1

1C =

nT

sim uun

(5) The corresponding coherency matrix is obtained by a special unitary transformation matrix.

simT

Reference: J. S. Lee and E. Pottier, Polarimetric radar imaging: from basics to applications. Boca Raton, FL: CRC Press, 2009.

/26


we computed the corresponding cumulative probability distribution curves of RMSE of all parameters for all the 216 different cases. Note that the cumulative distribution function is defined as

(a)

(b)

1

0 0.5 1 1.5 2 2.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(fv)

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(fs)

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

,XF x P X x where X represents RMSE

Results /26


(g) (h)

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(||)

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(angle())

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

(e) (f)

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(S)

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE(D

)C

um

ula

tive P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

(i)

1

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.180

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RMSE()

Cu

mu

lati

ve P

rob

ab

ilit

y

Modified Chen

GMD-GVSM

GMD-SAVSM

Results /26


Real Test-San Francisco, CA, USA

Radarsat2 C-band (Quad-Pol)

Date:

April 9, 2008

Original Resolution:

4.7m×4.8m

(range×azimuth)

Incidence Range:

28.45°~ 29.38°

AIRSAR L-band (Quad-Pol)

Date:

May 11, 1999

Original Resolution:

3.3m×9.26m

(range×azimuth)

Incidence Range:

28.4°~ 62.7°

/26


C-band Radarsat-2 Test

Figure Decomposition results from C-band Radarsat-2 data over San Francisco area. (a) Yamaguchi decomposition with orientation compensation; (b) Modified Chen decomposition by using modified parameter inversion algorithm; (c) Proposed GMD-GVSM decomposition; (d) Proposed GMD-SAVSM decomposition. The images are colored by Pd (red), Pv (green), Ps (blue).

(a) (b) (c) (d)

/26


L-band AIRSAR Test

Figure Decomposition results from AIRSAR-L data over San Francisco area. (a) Yamaguchi decomposition with orientation compensation; (b) Modified Chen decomposition by using modified parameter inversion algorithm; (c) Proposed GMD-GVSM decomposition; (d) Proposed GMD-SAVSM decomposition. The images are colored by Pd (red), Pv (green), Ps (blue).

(a) (b) (c) (d)

/26


Table Statistics of power of scattering components in selected areas with different land cover classes.

Statistics of power of scattering components

Area Methods Ps(%) Pd(%) Pv(%) Pc(%)

Forest

Y4R 35.98 13.72 45.21 5.09

Modified Chen 34.71 22.48 37.73 5.08

GMD–GVSM 32.48 21.02 41.40 5.10

GMD–SAVSM 33.81 21.76 39.30 5.12

Park

Y4R 29.31 11.51 53.21 5.97

Modified Chen 29.88 20.09 44.06 5.97

GMD–GVSM 26.47 18.75 48.79 5.99

GMD–SAVSM 28.77 19.80 45.40 6.03

Build-up A

Y4R 20.51 35.34 37.53 6.62

Modified Chen 20.10 40.57 32.74 6.59

GMD–GVSM 19.73 40.95 32.66 6.66

GMD–SAVSM 20.31 42.04 30.94 6.71

Build-up B

Y4R 33.48 48.35 14.15 4.01

Modified Chen 25.27 56.83 13.89 4.00

GMD–GVSM 26.54 55.15 14.29 4.01

GMD–SAVSM 27.75 53.53 14.68 4.04

Ocean

Y4R 95.12 1.86 2.60 0.41

Modified Chen 93.39 4.52 1.68 0.41

GMD–GVSM 93.33 4.52 1.74 0.41

GMD–SAVSM 93.01 4.43 2.15 0.41

1

C-band RadarSat-2 L-band AIRSAR

/26

Area Methods Ps(%) Pd(%) Pv(%) Pc(%)

Forest

Y4R 27.81 18.59 44.92 8.68

Modified Chen 27.36 29.30 34.66 8.68

GMD–GVSM 26.20 28.67 36.43 8.70

GMD–SAVSM 26.17 28.55 36.57 8.71

Park

Y4R 29.43 29.20 34.71 6.66

Modified Chen 24.93 40.21 28.21 6.65

GMD–GVSM 24.67 39.50 29.16 6.67

GMD–SAVSM 25.15 37.83 30.34 6.68

Build-up A

Y4R 30.99 37.60 24.74 6.67

Modified Chen 27.22 49.62 16.59 6.57

GMD–GVSM 27.34 49.11 16.96 6.59

GMD–SAVSM 26.03 47.18 20.20 6.59

Build-up B

Y4R 21.41 59.42 16.10 3.07

Modified Chen 15.53 68.22 13.19 3.06

GMD–GVSM 16.25 66.32 14.37 3.06

GMD–SAVSM 16.18 62.05 18.70 3.07

Ocean

Y4R 93.85 1.86 3.52 0.77

Modified Chen 91.82 5.35 2.06 0.77

GMD–GVSM 91.71 5.31 2.21 0.77

GMD–SAVSM 91.18 5.31 2.75 0.76

1


4, Conclusion




Outline


Conclusion

A number of Monte Carlo simulations shown the proposed ‘GMD-GVSM’ method

outperforms ‘GMD-SAVSM’ method and the previous existing strategies for PolSAR model-

based parameter inversion.

Both proposed methods provide a wide range of volume scattering models, which are

directly estimated by data themselves. In addition, they just need one non-linear optimization

calculation, which can significantly reduce both numerical issues and the computation cost.

As an overall conclusion we can state that the ‘GMD-GVSM’ method is a promising

methodology for quantitative target decompositions. More validations and applications need

be investigated in future.

The real test results indicate that radar frequency is an issue on affecting decomposition

results and developing more general decomposition methods is helpful for avoiding scattering

interpretation ambiguities.

/26


Thanks For Your Kind Attention!

Qinghua Xie1, 2 ([email protected]), J. David Ballester-Berman2,

Juan M. Lopez-Sanchez2, Jianjun Zhu1, Changcheng Wang1

1. School of Geosciences and Info-Physics, Central South University, Changsha, China

2. Institute for Computing Research (IUII), University of Alicante, Alicante, Spain

exploitation of general polarimetric model-based...

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