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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.
Candidates for wetland boundary classification
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
Candidates for wetland boundary classification
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 analysis
1. HH-VV phase difference
3. Correlation coefficient in LR basis
2. Double-bounce scattering (4-component model)
PolSAR image analysisL-band
Feb.
Aug.
Nov.
Pi-SAR
Winter
Summer
Autumn
illum
inat
ion
Lake “SAKATA”
VVHH Candidate 1:
+pi
0
PolSAR image analysis
1. HH-VV phase difference
3. Correlation coefficient in LR basis
2. Double-bounce scattering (4-component model)
PolSAR image analysisL-band
Feb.
Aug.
Nov.
Pi-SAR
Winter
Summer
Autumn
illum
inat
ion
Lake “SAKATA”
Ps
Pd
Pv
Candidate 2: dP
PolSAR image analysisL-band
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
Double-bounce scattering
Ground
Water
Double-bounce scattering
Reed
Surface scattering
Ground
Volume scattering
Surface scattering
L-bandPi-SAR
Ps (Surface scattering)Pd (Double-bounce scattering) Pv (Volume scattering)
Candidate 2: dP
PolSAR image analysisL-band
Feb.
Aug.
Nov.
Pi-SAR
Winter
Summer
Autumn
illum
inat
ion
Lake “SAKATA”
Ps
Pd
Pv
Candidate 2: dP
PolSAR image analysis
1. HH-VV phase difference
3. Correlation coefficient in LR basis
2. Double-bounce scattering (4-component model)
PolSAR image analysisL-band
Feb.
Aug.
Nov.
Pi-SAR
Winter
Summer
Autumn
illum
inat
ion
Lake “SAKATA”
Candidate 3: RRLL
1.0
0.0
PolSAR image analysisL-band
Feb.
Aug.
Nov.
Pi-SAR
Winter
Summer
Autumn
illum
inat
ion
Lake “SAKATA”
Candidate 3: RRLL
+pi
-pi
PolSAR image analysis
1. HH-VV phase difference
3. Correlation coefficient in LR basis
2. Double-bounce scattering (4-component model)
Polarimetric FDTD analysis
2. Double-bounce scattering (4-component model)
3. Correlation coefficient in LR basis
1. HH-VV phase difference
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. )
Polarimetric FDTD analysis
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
Polarimetric FDTD analysis
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
Polarimetric FDTD analysis
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
Polarimetric FDTD analysis
1. HH-VV phase difference
2. Double-bounce scattering (4-component model)
3. Correlation coefficient in LR basis
Polarimetric FDTD analysis
1. HH-VV phase difference
2. Double-bounce scattering (4-component model)
3. Correlation coefficient in LR basis
Polarimetric FDTD analysis
1. HH-VV phase difference
case1 case2 case3 case4 case5 case60.00
30.00
60.00
90.00
120.00
150.00
180.00
VVHH
Ave. 141o
So so!
Polarimetric FDTD analysis
1. HH-VV phase difference
2. Double-bounce scattering (4-component model)
3. Correlation coefficient in LR basis
Polarimetric FDTD analysis
Pd/Pt Ps/Pt Pv/Pt Pc/Pt0
0.2
0.4
0.6
0.8
1
2. Double-bounce scattering (4-component model)
The ensemble average processing is carried out
for 6 random distributed models.
Polarimetric FDTD analysis
Pd/Pt Ps/Pt Pv/Pt Pc/Pt0
0.2
0.4
0.6
0.8
1
Pd/Pt
Ps/Pt
Pv/Pt
Pc/Pt
2. Double-bounce scattering (4-component model)
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
Polarimetric FDTD analysis
Pd/Pt Ps/Pt Pv/Pt Pc/Pt0
0.2
0.4
0.6
0.8
1
Pd/Pt Ps/Pt Pv/Pt Pc/Pt0
0.2
0.4
0.6
0.8
1
with T33 rotation
2. Double-bounce scattering (4-component model)
Pd/Pt
Ps/Pt
Pv/Pt
Pc/Pt
w/o rotation
Polarimetric FDTD analysis
1. HH-VV phase difference
2. Double-bounce scattering (4-component model)
3. Correlation coefficient in LR basis
Polarimetric FDTD analysis
Amplitude Phase [deg.]
0.9130 -4.5044
The ensemble average processing is carried out
for 6 random distributed models.
3. Correlation coefficient in LR basis
Polarimetric FDTD analysis
Amplitude Phase [deg.]
0.9130 -4.5044
3. Correlation coefficient in LR basis
Man-made object :Phase tends to be 0 or 180 deg.
Man-made object :Amp. shows large value
Polarimetric FDTD analysis
3. Correlation coefficient in LR basis
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!