tu3.l09 - an overview of recent advances in polarimetric sar information extraction: algorithms and...
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AN OVERVIEW OF RECENT ADVANCES IN AN OVERVIEW OF RECENT ADVANCES IN POLARIMETRIC SAR INFORMATION EXTRACTION: POLARIMETRIC SAR INFORMATION EXTRACTION:
ALGORITHMS AND APPLICATIONSALGORITHMS AND APPLICATIONS
IGARSS 2010 - Hawaii IGARSS 2010 - Hawaii July 25 - July 30July 25 - July 30
Jong-Sen Lee*, Thomas AinsworthJong-Sen Lee*, Thomas AinsworthNaval Research laboratory Naval Research laboratory
Washington DC 20375, USAWashington DC 20375, USA
IntroductionIntroduction
• PolSAR information extraction technology has reached a certain degree of maturity.
• New PolSAR satellites:• ALOS/PALSAR – L-band• RADARSAT-2 – C-band• TERRASAR-X – X-band
• PolSAR textbooks (English): 2010, Cloude, Polarisation: applications in remote sensing. 2009, Lee and Pottier, Polarimetric Radar Imaging: from
basic to applications. 2008, Massonnet, and Souyris, Imaging with Synthetic
Aperture Radar. 2007, Mott, Remote Sensing with Polarimetric Radar.
• Golden age in developing PolSAR applications.
ALOS – PALSAR
. (Launched in January 2006)
Repeat cycle 46 days
(Tomakomai, Japan)
20 m x 20 m resolution
.
TerraSAR – X
Launched in June 15, 2007
Dual - Pol
(HH,VV), (HH,HV), (VH,VV)
Quad-Pol (Experimental)
Repeat cycle: 11 days
3 meter resolution
RADARSAT-2 (RS2)RADARSAT-2 (RS2)
C-Band Fine Quad-Pol Mode (8 m x 8 m resolution)
.
• Launched in December 14 2007
•24 days revisit cycle
Topics to be coveredTopics to be covered
• Review Advances in PolSAR information extraction for the last five years (TGRS, IGARSS).A) Target Decompositions/Orientation Angles,
B) Classification/Segmentation/Texture,
C) Calibration/Faraday Rotation
D) Speckle Filtering/Statistics,
E) Compact Polarimetry.
F) High-resolution PolSAR
G) Others: Forest / Vegetation, Ocean, surface parameters, bistatic, wetland, hard targets
• Not covered: • Pol-InSAR
• Polarimetric GPR
A)A) Target DecompositionsTarget Decompositions(Orientation Angles)(Orientation Angles)
H
Original (4-look) 5x5 9x9
H / A /H / A / VERSUS MULTI-LOOKINGVERSUS MULTI-LOOKING
A
Multi-look Effect on H/A/Multi-look Effect on H/A/
Cloude/ Pottier Decomposition
• Multi-look effect on
Lopez-Martinez (2005), Lee (2008) Entropy is underestimated, Anisotropy
overestimated Bias removal
// AH
Cloude/ Pottier DecompositionCloude/ Pottier Decomposition
• Alternative H and without eigenvalue and eigenvector computation (Praks, 2009)
• Applications: Forest (Garestier, 2009),P-band anisotropy related to forest height)
Oil Slick (Miliaccio, 2009), SIR-C, C-band
Freeman/Durden DecompositionFreeman/Durden Decomposition
FDD 3-component scattering model based decomposition
Issues: 1. More unknowns than equations
2. Reflection symmetry assumption
3. Negative power
Two-component decomposition from forest (Freeman, 2007)• Volume + (Surface or Double bounce) – 5 unknowns, 5 equation
334Tfv
Surface Double bounce Volume
100
010
002
4000
01
0||
||1000
0||
01
||1][ *
2
22
*
2vds fff
T
Volume(Canopy)
Double
Bounce
RoughSurfac
e
Freeman/Durden DecompositionFreeman/Durden Decomposition
4-component scattering model (Yamaguchi, 2005)
Surface Double bounce Volume Helix
10
10
000
2100
010
002
4000
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0||
||1000
0||
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||1][ *
2
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j
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T cvds
• T13 is not accounted for. (Lee, 2009) 5-component scattering model decomposition?
• Negative Power issue:• Orientation compensation reduces HV, that reduce negative power pixels (Lee, 2009, An, 2009,)• New volume scattering model (Yamaguchi, 2005)• New scacttering models and non-negative eigenvalues (van Zyl and Arii, 2009, 2010)
)()()()( RTSTRS mdm
jjT eee sinsincossincos
21
21tan,2sincossin2coscos
ssj
sms
j
smsT ejee
Touzi Decomposition (Touzi, 2007)
Cloude/Pottier:Symmetric Target
Touzi Pauli Basis:
2
1
0
0,
cossin
sincos)(,
cossin
sincos)(
dmm
mmm S
j
jTR
Kennaugh-Huynen
For asymmetric target
A)A) Polarization Orientation AnglesPolarization Orientation Angles
Polarization Orientation Angle (POA)
• Orientation angle effect on PolSAR images: (Lee and Schuler, 1999)
• Topography can affect scattering mechanisms
• HV power increased for high azimuth slopped surface
• Building not aligned along the azimuth direction
• HV power increased
• Point targets and random scatterers
• POA compensation is necessary for applications. If not,
• High azimuthal slopped surface – forest
• Buildings – forest
• Faraday rotation estimation by orientation angle (Kimura, 2008)
Urban (buildings) Orientation EffectsUrban (buildings) Orientation Effects
Freeman Decomposition
Orientation Angle
E-SAR
L-Band Dresden
The Effect of Radar FrequencyThe Effect of Radar Frequency
JPL AIRSAR Freiburg Forest, 15 June 1991
POLSAR Derived Orientation AnglesBY Circular Co-Pol Algorithm
P-Band P-Band Orientation Angles L-Band Orientation Angles
|HH-VV|, |HV|+|VH|, |HH+VV|
Polarization Orientation Angle
Camp Roberts, CA.
Polarization Orientation Angle (POA)
sincostan
tantan
3322
23 ]Re[2)4tan(
TT
T
FLIGHT
|HH-VV|, |HV|+|VH|, |HH+VV|
PO angles from C-band DEM C-Band DEM
L-Band PolSAR derived PO angle
PO angles derivedBy L-Band PolSAR
PO angles derivedfrom DEM of C-
Band interferometry
JPL AIRSAR L-Band PolSAR
3322
23 ]Re[2)4tan(
TT
T
POA Compensation – Coherency T POA Compensation – Coherency T (Lee,2010)(Lee,2010)
Rotation about LOS
POA Estimation by Circular Pol
Compensated results: 1) (= ) rotational invariant
2) (= ) always decreasing to minimum
3) (= ) consistently increasing because of pan and are roll invariant
TUTUT ~
2cos2sin0
2sin2cos0
001
U
11T 2/|| 2vvhh SS
33T2||2 HVS
2/|| 2vvhh SS 22T
11T
3322
23 ]Re[2)4tan(
TT
T
J.S. Lee and T.L. Ainsworth, “The effect of orientation angle compensation on coherency matrix and model-based decompositions”, IEEE TGRS, IGARSS2009 special issue, (in press).
POA Compensation – Coherency TPOA Compensation – Coherency T
3322
23 ]Re[2)4tan(
TT
T
• Compensated results: 4) (= ) rotational invariant 5) reduced to zero by PO compensation
6) Roll invariant
• Apply FDD after POA compensation: (Lee, 2009, An, 2009, Yamaguchi, IGARSS2010)
• Mitigating topography effect for PolSAR classification (Ainsworth, IGARSS2010)
]Im[ 23T
]Re[ 23T
])Im[( *HVVVHH SSS
213
212 |||| TT
TheThe POA Compensation on Diagonal Terms POA Compensation on Diagonal Terms
11T
33T
33T
,
22T
11T
Orientation angle map)4545( oo
33T
After
Before
B) Classification/Segmentation/TextureB) Classification/Segmentation/Texture
UNSUPERVISED CLASSIFIER (UNSUPERVISED CLASSIFIER (FREEMAN D. + WISHART)FREEMAN D. + WISHART)
|HH-VV|, |HV|, |HH+VV| 4th Iteration (15 classes)J.S. Lee, M.R. Grunes, E. Pottier, L. Ferro-Famil, “Unsupervised terrain classification preserving scattering characteristics,” IEEE Transactions on Geoscience and Remote Sensing,vol. 42, no.4, pp. 722-731, April, 2004.
DLR E-SAR L-Band Data
Freeman Decomposition Classification Map
Experimental Results – Oberphaffenhofen
Classification/Segmentation/TextureClassification/Segmentation/Texture
High-resolution PolSAR data makes Circular Gaussian or Wishart distributions seemingly insufficient for areas, such as, forest - Texture. (Ersahin, 2010, Lardeux, 2009, Doulgeris, 2008, Jager, 2007, Morio, 2007, Frery, 2007)
Classification: Assign a class for each pixel Segmentation: Partitioning the whole scene into regions of same attributes
(homogeneous areas). Texture model: The product model (For example, K-distribution)
• SLC
• Multi-look
g is the texture parameter, and can have many different pdfs. Issues:
• All three polarizations have the same distribution – frequently invalid• Multi-look reduce the texture effect.
ug
S
S
S
gy
VV
HV
HH
2
gZkukun
g= Y
n
1=k
)()( T*
Classification/Segmentation/TextureClassification/Segmentation/Texture
Wavelet texture model –(de Grandi, 2007)
Support Vector Machine: find a hyper plane to separate the training sets containing many polarimetric parameters (Lardex, 2009)
Minimizing Stochastical Complexity: partition the image by polygons of MSC (Mario, 2007)
Fuzzy H/alpha unsupervised classifier (Sang-Eun, 2007, Kersten, 2005).
Classification/Segmentation/TextureClassification/Segmentation/Texture
Issues involving evaluation of classification accuracy.• Ground truth map –
inhomogeneous training areas• For example, Urban, Park,
Ocean, Mountain - improper for classification evaluation
• Planting map for crop class.? Advantage of multi-frequency Wishart classifier remains
optimal for ‘homogeneous’ areas
(Lardex, 2009)
C) Calibration/ Faraday RotationC) Calibration/ Faraday Rotation
Calibration/ Faraday RotationCalibration/ Faraday Rotation
PolSAR calibration to compensate for Faraday rotation (Kimura 2009, Takeshiro 2009, Jehle 2009, Meyer 2008, Freeman )
ALOS/PALSAR, L-band are subject to ionospheric Faraday rotation.
Faraday rotation estimation algorithms:
• Circular right-left and left-right correlation (Meyer 2008)
• Based on orientation angle of buildings (Kimura 2009)
PALSAR calibration (Touzi, 2009)
Orientation angle perserving calibration (Ainsworth 2006)
)(4
1 * LRRL ZZArg44
Faraday RotationFaraday Rotation
• Circular right-left and left-right correlation
)(4
1 * LRRL ZZArg44
ALOS PALSAR, Gakona, Alaska
Pauli Faraday rotation
D) Speckle Filtering/ PolSAR StatisticsD) Speckle Filtering/ PolSAR Statistics
PolSAR Speckle FilteringPolSAR Speckle Filtering
Speckle reduction is necessary for classification, segmentation, target decomposition (H/A/), image analysis, etc.
“PolSAR Speckle Filtering” also known as
“Coherency Matrix Estimation”
“Polarimetric Parameter Estimation” (Vasil, IGARSS2010)
Basic principle: Preserve scattering characteristics (coherency or covariance matrix)
• Select neighboring pixels of the same scattering property
• Filter each element of the matrix equally and independently
• Different opinion (Lopez-Martinez, 2008, Foucher and Lopez-Martinez, IGARSS2010)
• Increase correlations of off-diagonal elements – wavelet
PolSAR Speckle FilteringPolSAR Speckle Filtering
Intensity-Driven Adaptive Neighborhood - region grow (Vasile, 2006)
• Bias due to applying sigma filter
Speckle filtering based on classification map
• Preserving scattering mechanism (Lee, 2006)
Improved sigma filter (Lee 2008)
• Filter distributed target by
an improved sigma filter – no bias
• Preserving point (high-return) targets in HH+VV, HH-VV and HV
XXX
X
X
• zc > 98 percentile z98 • Number of z98 pixels ≥ 5 in a 3x3 window
Improved Sigma FilterImproved Sigma Filter
|HH-VV|, |HV|, |HH+VV|
Original
5x5Sigma
Filtered(Lee, IGARSS2008)
PolSAR Speckle Filtering/ PolSAR StatisticsPolSAR Speckle Filtering/ PolSAR Statistics
• Speckle filtering is not an exact science. The filtering requirements depend on
• Applications
• Personal preference
• Comparison of PolSAR filters
• Foucher et al (IGARSS2009)
• PolSAR Statistics
• Correlation term has the combination of multiplicative and additive noise depending on coherence – extension to multi-look data (Lopes-Martinez, 2007)
• PDF for normalized coherency matrix (Vasile, 2010)
E) Compact PolarimetryE) Compact Polarimetry
Compact PolarimetryCompact Polarimetry
• Alternative Dual-Pol SAR system: Transmitting a single polarization (/4, circular) and receiving two orthogonal polarizations (H and V, CR and CL). Additional assumptions required for pseudo quad-pol reconstruction.
• Reduce pulse repetition frequency – double swath width
• Simplify SAR system
• The /4 mode (Souyris, 2005) named it “compact polarimetry”
• Transmit at 45 polarization and receiving (H,V)
• Modes: /4, CR transmit dual Circular Receiving, CR transmit (H,V) Receiving (Souyris, Stacy, Nord, Dubois-Fernandez, Raney)
2
22
24/
HVVVHVHHCTLR
VVHHHVvvHHRLRRDCP
HVVVHVHH
SiSiSSk
SSiSiSSSSk
SSSSk
Compact PolarimetryCompact Polarimetry
• Consensus: Transmit Circular and receiving (H, V)
• Transmit circular and receive (CR, CL) for ionosphere
• Pseudo quad-pol reconstruction
• Reflection symmetry assumption
• Additional identity is required
• Souyris, 2005
• Nord and Ainsworth, 2009
22
*
22
2
,14
1
VVHH
VVHH
VVHH
HV
SS
SS
SS
S
2
222
21
HV
VVHHVVHH
HV
S
SSSS
S
Compact PolarimetryCompact Polarimetry
• Incomplete polarimetric measurements
• CP measures only 4 parameters
• Quad-pol measures 9 parameters
• Reconstruction is unreliable
• |HV| reconstruction
• Polarization orientation angle can not be measured, especially for distributed targets
• Target decompositions: H/A/, Model-based decompositions
• Hardware issues of transmitting perfect circular pol
• Summary: Compact polarimetry does not replace quad-pol in acquiring polarimetric information.
(Boerner, IGARSS2010)
F) High Resolution PolSARF) High Resolution PolSAR
FSAR – “Future” Airborne SAR
X-Band, PolSAR 2-Look, 0.5 m resolution, VV, HV, HH
Images courtesy of Dr. Andreas Reigber, DLR, Germany
FSAR S-Band
Partial ReferencesPartial References
A) Target Decompositions, Orientation Angles [1] Wentao An, Yi Cui, Jian Yang, “Three-Component Model-Based Decomposition for Polarimetric SAR Data,” IEEE TGRS,
vol.48, June 2010.[2] Ballester-Berman, J.D., Lopez-Sanchez, J.M., “
Applying the Freeman–Durden Decomposition Concept to Polarimetric SAR Interferometry,” IEEE TGRS, January 2010.[3] Touzi, R., Deschamps, A., Rother, G., “
Phase of Target Scattering for Wetland Characterization Using Polarimetric C-Band SAR,” IEEE TGRS, vol. 47, September 2009.
[4] Praks, J., Koeniguer, E.C., Hallikainen, M.T., “Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry,” IEEE TGRS , vol. 47, July 2009.
[5] Lee, J.S., Ainsworth, T.L., Kelly, J.P., Lopez-Martinez, C., “Evaluation and Bias Removal of Multilook Effect on Entropy/Alpha/Anisotropy in Polarimetric SAR Decomposition,” IEEE TGRS, vol. 46, October 2008.
[6] Yajima, Y., Yamaguchi, Y., Sato, R., Yamada, H., Boerner, W.-M, “POLSAR Image Analysis of Wetlands Using a Modified Four-Component Scattering Power Decomposition,” IEEE TGRS, vol.46, June 2008.
[7] Freeman, A., “Fitting a Two-Component Scattering Model to Polarimetric SAR Data from Forests,” IEEE TGRS, vol. 45, August 2007.
[8] Touzi, R., “Target Scattering Decomposition in Terms of Roll-Invariant Target Parameters,” IEEE TGRS, vol. 45, January 2007.
[9] Cameron, W.L., Rais, H., “Conservative Polarimetric Scatterers and Their Role in Incorrect Extensions of the Cameron Decomposition,” IEEE TGRS, vol. 44, December 2006.
[10] Lopez-Martinez, C., Pottier, E., Cloude, S.R., “Statistical Assessment of Eigenvector-Based Target Decomposition Theorems in Radar Polarimetry,” IEEE TGRS, vol. 43, September 2005.
[11] Yamaguchi, Y., Moriyama, T., Ishido, M., Yamada, H., “Four-component scattering model for polarimetric SAR image decomposition,” IEEE TGRS, vol. 43, August 2005.
[12] Iribe, K., Sato, M., “Analysis of Polarization Orientation Angle Shifts by Artificial Structures,” IEEE TGRS, vol.45, November 2007
[13] Marino, A., Cloude, S.R., Woodhouse, I.H., “A Polarimetric Target Detector Using the Huynen Fork,” IEEE TGRS, vol.48, May 2010.
[14] M. Arii, J.J. van Zyl, Y. Kim, “Adaptive decomposition of polarimetric SAR covariance matrix,” presented at IGARSS’2009, Cape Town, South Africa, July 2009.
[15] Lee, J.-S., Thomas L. Ainsworth, Kun-Shan Chen, “The effect of orientation angle compensation on polarimetric target decompositions,” Proceedings of IGARSS’2009, Cape Town, South Africa, July 2009.
Partial ReferencesPartial References
B. Classification/Segmentation/ Texture
[1] Ersahin, K., Cumming, I.G., Ward, R.K, “Segmentation and Classification of Polarimetric SAR Data Using Spectral Graph Partitioning,” IEEE TGRS, vol. 47, January 2010
[2] Lardeux, C., Frison, P.-L., Tison, C., Souyris, J.-C., Stoll, B., Fruneau, B., Rudant, J.-P, “Support Vector Machine for Multifrequency SAR Polarimetric Data Classification,” IEEE TGRS vol.47, December 2009.
[3] Doulgeris, A.P., Anfinsen, S.N., Eltoft, T., “Classification with a Non-Gaussian Model for PolSAR Data,” IEEE TGRS, vol.46, October 2008.
[4] De Grandi, G.D., Lee, J.S., Schuler, D.L, “Target Detection and Texture Segmentation in Polarimetric SAR Images Using a Wavelet Frame: Theoretical Aspects,” IEEE TGRS, vol.45, November 2007.
[5] Jager, M., Neumann, M., Guillaso, S., Reigber, A., “A Self-Initializing PolInSAR Classifier Using Interferometric Phase Differences,” IEEE TGRS, vol.45, November 2007.
[6] Morio, J., Goudail, F., Dupuis, X., Dubois-Fernandez, P.C., Refregier, P., “Polarimetric and Interferometric SAR Image Partition Into Statistically Homogeneous Regions Based on the Minimization of the Stochastic Complexity,” IEEE TGRS, vol.45, November 2007.
[7] Frery, A.C., Correia, A.H., da Freitas, C.D., “Classifying Multifrequency Fully Polarimetric Imagery With Multiple Sources of Statistical Evidence and Contextual Information,” IEEE TGRS, vol.45, October 2007
C. Calibration and Faraday Rotation
[1] Kimura, H., “Calibration of Polarimetric PALSAR Imagery Affected by Faraday Rotation Using Polarization Orientation,” IEEE TGRS vol.48, December 2009
[2] Touzi, R., Shimada, M., “Polarimetric PALSAR Calibration,” IEEE TGRS, vol.48, December 2009[3] Takeshiro, A., Furuya, T., Fukuchi, H., “
Verification of Polarimetric Calibration Method Including Faraday Rotation Compensation Using PALSAR Data,” IEEE TGRS, vol.47, December 2009
[4] Jehle, M., Ruegg, M., Zuberbuhler, L., Small, D., Meier, E., “Measurement of Ionospheric Faraday Rotation in Simulated and Real Spaceborne SAR Data,” IEEE TGRS, vol. 47, May 2009.
[5] Meyer, F.J., Nicoll, J.B., “Prediction, Detection, and Correction of Faraday Rotation in Full-Polarimetric L-Band SAR Data,” IEEE TGRS, vol. 46, October 2008.
[6] Ren-Yuan Qi, Ya-Qiu Jin, “Analysis of the Effects of Faraday Rotation on Spaceborne Polarimetric SAR Observations at P-Band,” IEEE TGRS, vol. 45, may 2007.
[7] Ainsworth, T.L., Ferro-Famil, L., Jong-Sen Lee, “Orientation angle preserving a posteriori polarimetric SAR calibration,” IEEE TGRS, vol. 44, April 2006.
Partial ReferencesPartial References
D. Speckle Filtering and PolSAR Statistics
[1] Vasile, G., Ovarlez, J.-P., Pascal, F., Tison, C., “Coherency Matrix Estimation of Heterogeneous Clutter in High-Resolution Polarimetric SAR Images ,” IEEE TGRS, vol.48, April 2010.
[2] Lopez-Martinez, C., Fabregas, X., “Model-Based Polarimetric SAR Speckle Filter,” IEEE TGRS, November 2008.
[3] Lopez-Martinez, C., Pottier, E., “On the Extension of Multidimensional Speckle Noise Model From Single-Look to Multilook SAR Imagery ,” IEEE TGRS, February 2007.
[4] Vasile, G., Trouve, E., Jong-Sen Lee, Buzuloiu, V., “Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation,” IEEE TGRS, vol. 44, June 2006.
[5] Jong-Sen Lee, Grunes, M.R., Schuler, D.L., Pottier, E., Ferro-Famil, L., “Scattering-model-based speckle filtering of polarimetric SAR data,” IEEE TGRS, vol. 44, January 2006.
[6] S. Foucher, C. Lopez-Martinez, G. Farage, “An Evaluation of PolSAR Speckle Filters,” Proceedings of IGARSS’2009, Cape Town, South Africa, July 2009.
[9] Lee, JS, T.L. Ainsworth, K.S. Chen, “Speckle filtering of dual-pol and polarimetric SAR data based on improved sigma filter,” Proceedings of IGARSS2008, Boston, USA, 2008.
E. Compact Polarimetry
[1] Nord, M.E., Ainsworth, T.L., Jong-Sen Lee, Stacy, N., “Comparison of Compact Polarimetric Synthetic Aperture Radar Modes,” IEEE TGRS, February 2009.
[2] Dubois-Fernandez, P.C., Souyris, J.-C., Angelliaume, S., Garestier, F., “The Compact Polarimetry Alternative for Spaceborne SAR at Low Frequency,” IEEE TGRS, Vol. 46, October 2008.
[3] Raney, R.K. “Hybrid-Polarity SAR Architecture,” IEEE TGRS, vol. 45, November 2007.[4] Souyris, J.-C., et al., “
Compact polarimetry based on symmetry properties of geophysical media: the π/4 mode ,” IEEE TGRS, vol. 43, March 2005.
Partial ReferencesPartial References
F. Forest/Vegetation
[1] Neumann, M., Ferro-Famil, L., Reigber, A., “Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data,” IEEE TGRS, vol. 48, March 2010.
[2] Garestier, F., Dubois-Fernandez, P.C., Guyon, D., Le Toan, T., “Forest Biophysical Parameter Estimation Using L- and P-Band Polarimetric SAR Data,” IEEE TGRS, vol. 47, October 2009.
[3] Haipeng Wang, Ouchi, K., “Accuracy of the K-Distribution Regression Model for Forest Biomass Estimation by High-Resolution Polarimetric SAR: Comparison of Model Estimation and Field Data,” IEEE TGRS, vol. 46, April 2008.
[4] Watanabe, M., et al., “Forest Structure Dependency of the Relation between L-Band and Biophysical Parameters,” IEEE TGRS, vol. 44, November 2006.
[5] Lopez-Sanchez, J.M., et al., “Indoor wide-band polarimetric measurements on maize plants: a study of the differential extinction coefficient,” IEEE TGRS, vol. 44, April 2006.
[6] McNeill, S., Pairman, D., “Stand age retrieval in production forest stands in New Zealand using C- and L-band polarimetric Radar,” IEEE TGRS, vol.43, November 2005.
G. Ocean Applications, Ship and Sea Ice Detection
[1] Migliaccio, M., Gambardella, A., Nunziata, F., Shimada, M., Isoguchi, O., “The PALSAR Polarimetric Mode for Sea Oil Slick Observation,” IEEE TGRS vol.47, December 2009
[2] Margarit, G., Mallorqui, J.J., Fortuny-Guasch, J., Lopez-Martinez, C., “Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions,” IEEE TGRS, April, 2009
[3] Migliaccio, M., Gambardella, A., Tranfaglia, M., “SAR Polarimetry to Observe Oil Spills,” IEEE TGRS, vol. 45, February 2007.
[4] Margarit, G., Mallorqui, J.J., Rius, J.M., Sanz-Marcos, J., “On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies,” IEEE TGRS, vol. 44, December 2006.
[5] Nakamura, K., Wakabayashi, H. et al., “Observation of sea-ice thickness in the sea of Okhotsk by using dual-frequency and fully polarimetric airborne SAR (pi-SAR) data,” IEEE TGRS, vol. 43, November 2005.
Partial ReferencesPartial References
H. Surface Parameter Estimation
[1] Yunjin Kim, van Zyl, J.J, “A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data,” IEEE TGRS, August 2009.
[2] Sang-Eun Park, Moon, W.M. Duk-jin Kim, “Estimation of Surface Roughness Parameter in Intertidal Mudflat Using Airborne Polarimetric SAR Data,” IEEE TGRS, vol. 47, May 2009
[3] Hajnsek, I., Jagdhuber, T., Schon, H., Papathanassiou, K.P., “Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR,” IEEE TGRS vol. 47, February 2009.
I. Bistatic PolSAR
[1] Titin-Schnaider, C., “Physical Meaning of Bistatic Polarimetric Parameters,” IEEE TGRS, vol.48, May 2010.
[2] Feng Xu, Ya-Qiu Jin, “Imaging Simulation of Bistatic Synthetic Aperture Radar and Its Polarimetric Analysis,” IEEE TGRS, vol. 46, August 2008.
[3] Titin-Schnaider, C., “Polarimetric Characterization of Bistatic Coherent Mechanisms,” IEEE TGRS, vol. 46, May 2008.
[4] Souyris, J.-C., Tison, C., “Polarimetric Analysis of Bistatic SAR Images From Polar Decomposition: A Quaternion Approach,” IEEE TGRS, Vol. 45, September 2007.
J. Target Detection and Analysis
[1] Margarit, G., Mallorqui, J.J., Pipia, L., “Polarimetric Characterization and Temporal Stability Analysis of Urban Target Scattering.” IEEE TGRS, vol. 48, April, 2010
[2] Marquart, N.P., Molinet, F., Pottier, E., “Investigations on the polarimetric behavior of a target near the soil,” IEEE TGRS, vol.44, October 2006.
K. Other Applications[1] Suwa, K. Iwamoto, M., “A Two-Dimensional Bandwidth Extrapolation Technique for Polarimetric
Synthetic Aperture Radar Images,” IEEE TGRS, vol.45, January 2007.[2] Schneider, R.Z. Papathanassiou, K.P. Hajnsek, I. Moreira, A., “Polarimetric and interferometric
characterization of coherent scatterers in urban areas,” IEEE TGRS, Vol. 44, April 2006.
Partial ReferencesPartial References
K. Other Applications
[1] Suwa, K. Iwamoto, M., “A Two-Dimensional Bandwidth Extrapolation Technique for Polarimetric Synthetic Aperture Radar Images,” IEEE TGRS, vol.45, January 2007.
[2] Schneider, R.Z. Papathanassiou, K.P. Hajnsek, I. Moreira, A., “Polarimetric and interferometric characterization of coherent scatterers in urban areas,” IEEE TGRS, Vol. 44, April 2006.
L. PolSAR Textbooks (in English)
[1] Cloude, S.R., Polarisation: applications in remote sensing, Oxford University Press, Oxford, New York, 2010.
[2] Lee, J.S. and Pottier, E., Polarimetric Radar Imaging: from basic to applications, Taylor & Francis/CRC Press, Boca Raton, London, New York, 2009.
[3] Massonnet, D. and Souyris, J-C, Imaging with Synthetic Aperture Radar, , Taylor & Francis/CRC Press, Boca Raton, London, New York, 2008.
[4] Mott, H., Remote Sensing with Polarimetric Radar, Wiley & Sons, New Jersey, 2007.
ConclusionConclusion
• PolSAR information extraction research has reach a certain degree of maturity.
• The availability of space borne and airborne PolSAR data will stimulate applications and developing advanced information extraction algorithms.
• TanDEM-X Mission: Bistatic PolSAR research
• High resolution (less than 1 m) PolSAR will open up new area of research and applications.
• ALOS/PALSAR, and RADARSAT-2 follow ups, and TerraSAR-L
• PolSAR research has a bright future