polarimetric scattering feature estimation for accurate wetland boundary classification

Polarimetric Scattering Feature Estimation For Accurate Wetland

Boundary Classification

Ryoichi SATO*, Yoshio YAMAGUCHI, and Hiroyoshi YAMADA

Niigata University, Japan

IGARSS 2011, July 24-29, 2011, Vancouver, Canada

Copyright © 2001-2004 Niigata City. All rights reserved.

Lake “Sakata” and surrounding wetland

Winter

(Forests, wetlands, etc.)

Introduction

- Natural disasters

Monitoring of “Natural resources”

(Flooding, Water shortage)

- Unusual weather (Climate change)

Progress of Global warming

Introduction

Pi-SAR

http://www.das.co.jp/new_html/service/05.html

Airborne PolSAR

“PolSAR image analysis” is a useful tool for continuous wetland monitoring

Copyright © 2001-2004 Niigata City. All rights reserved.

Summer

http://www.alos-restec.jp/aboutalos1.html

ALOS/PALSAR

Satellite PolSAR

Accurate and “complex” wetland classification method So far,

Objective

2. FDTD polarimetric scattering analysis for a simple water-emergent boundary model Verification of the generating mechanism of specific polarimetric scattering feature at the boundary

``Simple’’ water area classification marker for water-emergent boundary

1. PolSAR image analysis around wetland area Validity of some polarimetric indices as useful markers for water-emergent boundary classification

Candidates for wetland boundary classification

Looks like Dihedral reflector

Water

Reed

Ground

1. HH-VV phase difference:

bc

ca

SS

SSS

VVVH

HVHHbasisHV _

)( HHVV

HH

VV

i

HH

VVi

HH

iVV

HH

VV eS

S

eS

eS

S

S

VVHH

[1] K.O. Pope, et al. ,``Detecting seasonal flooding cycles in marches of the yucatan peninsula with sar-c polarimetric radar imagery,’’ Remote Sensing Environ., vol.59, no.2 pp.157-166, Feb.1997.


Looks like Dihedral reflector

2. Double-bounce scattering: dP

helixcvolumevdoubledsurfaces TfTfTfTfT

PdPs Pv

Water

Reed

Surface scattering

Volume scattering

Double-bounce scattering

Ground

[5] A. Freeman and S.L.Durden,``A three-component scattering model for polarimetric SAR data,’’ IEEE Trans. Geosi. Remote Sensiing, vol.36, no.3 pp.963-973, May 1998.

[6] Y. Yamaguchi et al, ``Four-component scattering model for polarimetric SAR image decomposition,’’ IEEE Trans. Geosi. Remote Sensiing, vol.43, no.8 pp.1699-1706, Aug. 2005.

Pc

TRUE Water area


3. LL-RR correlation coefficient: RRLL

[Kimura 2004] K. Kimura, et al. ,``Circular polarization correlation coefficient for detection of non-natural targets aligned not parallel to SAR flight path in the X-band POLSAR image analysis,’’ vol.E87-B, no.10 pp.3050-3056, Oct.2004.

22

*22

22

*

22

)(Re44),(

cjbacjba

bacjbac

SS

SSRRLLCor

RRLL

RRLL

RRLL

22

*

1

4

)(Re4tan

cba

bacRRLL

[Schuler 2006] D. Schuler, J.-S. Lee, and G.D.DeGrande, ``Characteristics of polarimetric SAR scattering in urban and natural areas,'' Proc. of EUSAR 2006 (CD-ROM), May 2006. .

PolSAR image analysis

1. HH-VV phase difference

3. Correlation coefficient in LR basis

2. Double-bounce scattering (4-component model)

PolSAR data description

Mode: Quad.Pol. HH+HV+VH+VV

Pi-SAR & ALOS/PALSAR

Quad. polarimetric data take function

Pi-SAR* ALOS/PALSAR**

Resolution 3.0m by 3.0m (L-band) 30m by 30m

Total pixel number (entire region)

2,000 by 2,000 (L-band) 1,248 by 18,432

Averaging size (pixels) 5 by 5 1 by 6

Incident angle [deg.] 02/08/2004 31.71-46.13 08/04/2004 30.19-44.18 11/04/2004 31.19-45.49

21.5 (Off Nadir angle)

Winter

Summer

Autumn

L-band 1.27GHz (l=0.236m)

**Acquired by JAXA, Japan

* Acquired by JAXA, Japan

Lake “SAKATA”

PolSAR image analysisL-band

Feb.

Aug.

Nov.

Pi-SAR

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

VVHH Candidate 1:

+pi

0


Feb.

Aug.

Nov.

Pi-SAR

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

Ps

Pd

Pv

Candidate 2: dP


Feb.

Aug.

Nov.

Pi-SAR

A

B

A

B

A

B

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

Ps

Pd

Pv

Candidate 2: dP

PolSAR image analysisEmergent(Reeds)

Water

TRUE Water area

Winter

Summer

Autumn

Water

Double-bounce scatteringReed

Surface scattering

Volume scattering


Ground

Water


Reed

Surface scattering

Ground

Volume scattering

Surface scattering

L-bandPi-SAR

Ps (Surface scattering)Pd (Double-bounce scattering) Pv (Volume scattering)

Candidate 2: dP


Feb.

Aug.

Nov.

Pi-SAR

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

Ps

Pd

Pv

Candidate 2: dP


Feb.

Aug.

Nov.

Pi-SAR

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

Candidate 3: RRLL

1.0

0.0


Feb.

Aug.

Nov.

Pi-SAR

Winter

Summer

Autumn

illum

inat

ion

Lake “SAKATA”

Candidate 3: RRLL

+pi

-pi

Polarimetric FDTD analysis




Polarimetric FDTD analysisPolarimetric scattering analysis for simple boundary model

by using the FDTD method

High water level case

Awhere

Vertical thin dielectric pillars on a dielectric plate

Dielectric pillars(vertical stems of the emergent plants)

Dielectric plate (Water)

is added to reduce unnecessary back scattering from the horizontal front edge.

(Vertical stems of emerged-plants 　 on water surface when the water level is relatively high. )


High water level case

To determine the relative permittivity for the dielectric base plate or water in the model, the actual relative permittivity of the water in “SAKATA” is measured

by a dielectric probe kit (Agilent 85070C).

er = 82.78 + i 8.01

at 1.2GHz


Analytical region

Cubic cell size DTime step Dt

Incident pulse

Absorbing boundary condition

1200 X 1200 X 1000 cells

0.0025m

4.8125 X 10-12 s

Lowpass Gaussian pulse

PML (8 layers)

Other parameters in the FDTD simulation

L=9.6l (2.40m), H1=5.6l (1.40m), D1=2.4l (0.60m), D2=3.40l (0.85m) at 1.2GHz

e r = 2.0 + i 0.05

at 1.2GHz

1cm

1cm

Each dielectric pillar

Parameters in the FDTD analysis

=f f0=0o

=q q0=45o

Plain view


To evaluate statistical polarimetric scattering feature as actual PolSAR image analysis,

The ensemble average processing is carried out

for 6 random distributed patterns.

Vertical pillars are randomly set on dielectric plate

Statistical evaluation



case1 case2 case3 case4 case5 case60.00

30.00

60.00

90.00

120.00

150.00

180.00

VVHH

Ave. 141o

So so!


Pd/Pt Ps/Pt Pv/Pt Pc/Pt0

0.2

0.4

0.6

0.8

1



for 6 random distributed models.



0.2

0.4

0.6

0.8

1

Pd/Pt

Ps/Pt

Pv/Pt

Pc/Pt


Very useful

Pt=Pd+Pv+Ps+Pc

``Unitary rotation’’ possible

``Unitary rotation’’ of the original coherency matrix

2cos2sin0

2sin2cos0

001

2cos2sin0

2sin2cos0

001

TT

04cos}Re{44sin)(2)( 23332233 TTTT

Condition for determining the rotation angle

3322

231 }Re{2tan2

12

TT

T

So we obtain the rotation angle as



0.2

0.4

0.6

0.8

1


0.2

0.4

0.6

0.8

1

with T33 rotation


Pd/Pt

Ps/Pt

Pv/Pt

Pc/Pt

w/o rotation


Amplitude Phase [deg.]

0.9130 -4.5044


for 6 random distributed models.



Amplitude Phase [deg.]

0.9130 -4.5044


Man-made object :Phase tends to be 0 or 180 deg.

Man-made object :Amp. shows large value



0** HVVVHVHH SSSS 0** bcacReflection symmetry

22

22

4

4),(

bac

bacRRLLCorRRLL

i.e.

Phase

Real

0 or p

This condition is derived from experimental results.

Amplitude

ConclusionTo verify three polarimetric indices

as simple wetland boundary classification markers

PolSAR image analysis and FDTD polarimetric scattering analysis

for wetland boundary (water-emergent ) model

``qHH-qVV” ,``Pd” and gLL-RR are ALL useful markers, when the water level is relatively high.

Future developments

1. Variation of the incident and squint angles

2. Variation of the volume density

3. Difference between wet and dry conditions

- FDTD polarimetric scattering analysis

Dielectric plate (Water)

- Comparison with accurate method (Touzi decomposition etc.)

Which wetland classes in Touzi decomposition correspond to each boundary feature?

Acknowledgments

This research was partially supported by - A Scientific Research Grant-In-Aid (22510188) from JSPS , -Telecom Engineering Center (TELEC)

Thank you!

polarimetric scattering feature estimation for accurate wetland boundary classification

Technology

polsar image analysis1

fdtd polarimetric

polarimetric fdtd analysis1

polarimetric data

xband polsar image analysis

hhvv phase difference2

component model3

polarimetric sar data