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

01

0||

||1000

0||

01

||1][ *

2

22

*

2

j

jffff

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