SlideShare a Scribd company logo
1 of 18
Download to read offline
FACULTY OF SCIENCE AND TECHNOLOGY
DEPARTMENT OF PHYSICS AND TECHNOLOGY



  SEGMENTATION OF POLARIMETRIC SAR
  DATA WITH A MULTI-TEXTURE PRODUCT
  MODEL



  Anthony P. Doulgeris,
  S. N. Anfinsen, and T. Eltoft


  IGARSS 2012
Background: IGARSS 2011
Presented a multi-texture model for PolSAR data

 • Probability Density Function for Scalar and Dual-texture
   models

 • Log-cumulant expressions for all multi-texture models

 • Hypothesis tests to determine most appropriate multi-texture
   model

Showed evidence for multi-texture from manually chosen box-
window estimates



                             2 / 17
Objective 2012
Place multi-texture models into an advanced segmentation
algorithm

 • Hypothesis tests to choose between Scalar or Dual-texture
   models

 • U -distribution for flexible texture modelling

 • Log-cumulants for parameter estimation

 • Goodness-of-fit testing for number of clusters

 • Markov Random Fields for contextual smoothing

Show real multi-texture segmentation results.
                               3 / 17
Scalar-texture
Scattering vector:
                     s = [shh; shv ; svh; svv ]t
Scalar product model:             √
                            s=           tx
where

texture variable     t ∼ Γ(1, α), Γ−1(1, λ), or F(1, α, λ)
                          c
speckle variable     x ∼ Nd (0, Σ)

1. Scalar t modulates all channels equally.
2. Speculation: scattering mechanisms impact specific channels and
   may lead to different textural characteristics per channel.

                                4 / 17
Multi-texture
Scattering vector:
                       s = [shh; shv ; svh; svv ]t
Multi-texture product model:
                              s = T1/2x
where
                     T = diag{thh; thv ; tvh; tvv }


Special cases:
Scalar-texture   t = thh = thv = tvh = tvv

Dual-texture     tco = thh = tvv and tcross = thv = tvh

                                  5 / 17
Multi-texture PDF
Given
                 s = T1/2x
                         L
                    1
                C =            sisH = T1/2CxT1/2
                                  i
                    L
                         i=1

                   LLd|C|L−d etr(−LΣ−1T−1/2CT−1/2)
                                     x
fC|T(C|T; L, Σx) =
                            Γd(L)|T|L|Σx|L
Then

        fC(C; L, Σx) =    fC|T(C|T; L, Σx)fT(T)dT



                               6 / 17
Dual-texture Case
- Reciprocal and reflection symmetric assumptions

                     L3L |C|L−3
fC(C; L, Σx) =      Γ3(L) |Σx|L

          1                q11c11+q14c41+q41c14+q44c44
    ×    t2L exp −L                     tco               ftco (tco) dtco
          co

           1                q22c22+q23c32+q32c23+q33c33
    ×    t2L      exp −L               tcross             ftcross (tcross)dtcross
          cross



where
qij denotes the ij th elements of Σ−1
                                   s
ftco (tco) and ftcross (tcross) denotes the PDFs of tco and tcross,
respectively.

                                   7 / 17
Multi-texture Log-Cumulants
Scalar: κν {C} = κν {W} + dν κν {T }
Dual: κν {C} = κν {W} + dν κν {Tco} + dν κν {Tcross}
                         co            cross


(Scaled) Wishart-distribution (L, Σ)
                      (0)
                   ψd (L) + ln |Σ| − d ln(L) , ν = 1
      κν {W} =      (ν−1)
                   ψd (L)                    ,ν > 1

F -distribution (α, λ)
                  ψ (0)(α) − ψ (0)(λ) + ln( λ−1 ) , ν = 1
                                             α
      κν {T } =
                  ψ (ν−1)(α) + (−1)ν ψ (ν−1)(λ) , ν > 1
Texture Parameters:
Scalar (α, λ)                 Dual (αco, λco) & (αcross, λcross)
                             8 / 17
Multi-texture Hypothesis Test
Scalar: κν {C} = κν {W} + dν κν {T }
Dual: κν {C} = κν {W} + dν κν {Tco} + dν κν {Tcross}
                         co            cross
Estimate texture parameters for T , Tco and Tcross
Choose from smallest of Dscalar or Ddual
                   10
                                  o
                    9

                    8
                                           Dscalar

                    7
                                                  X
                    6
         Kappa 2




                                                      Ddual
                    5                             o
                    4

                    3

                    2

                    1
                                                       W
                   0
                   −5   −4   −3       −2     −1          0      1   2   3   4   5
                                                      Kappa 3



                                                  9 / 17
Segmentation Algorithm
Iterative expectation maximisation algorithm with a few
modifications, scalar-texture version detailed in
    Doulgeris et al. TGRS-EUSAR (2011) and
    Doulgeris et al. EUSAR (2012).
The key features are:
 • U -distribution for flexible texture modelling
 • Log-cumulants for parameter estimation
 • Goodness-of-fit testing for number of clusters
 • Markov Random Fields for contextual smoothing
   Hypothesis tests to choose Scalar or Dual-texture model

                               10 / 17
Segmentation Algorithm → Multi-texture
Iterative expectation maximisation algorithm with a few
modifications, scalar-texture version detailed in
    Doulgeris et al. TGRS-EUSAR (2011) and
    Doulgeris et al. EUSAR (2012).
The key features are:
 • U -distribution for flexible texture modelling → Multi-texture
 • Log-cumulants for parameter estimation → Multi-texture
 • Goodness-of-fit testing for number of clusters
 • Markov Random Fields for contextual smoothing
 • Hypothesis tests to choose Scalar or Dual-texture model

                              10 / 17
Simulated 8-look Data
            K-Wishart                U-distribution
  Class 1
            α = 15                   α = 16.5, λ = 4220
            Co-pol
            K-Wishart α = 10         α = 10.4, λ = 217
  Class 2
            Cross-pol
            G 0 λ = 30               α = 4220, λ = 28.8




                     Lexicographic RGB
                           11 / 17
Simulated Results



  (a) Lexicographic RGB                (b) Class segmentation




                                      (d) Class log-cumulants




   (c) Class histograms             (e)Dual-texture log-cumulants
                          12 / 17
Real Data 1: San Francisco City
Radarsat-2 sample image from 9 April, 2008, 25-looks.




                            13 / 17
Real Data 2: Amazon Rainforest
ALOS PALSAR sample data from 13 March, 2007, 32-looks.




                          14 / 17
NO MULTI-TEXTURE !
But previously ...            15

                              10

                               5
                                                                                                       µ=   0.881



                               0
                                   0               0.5          1       1.5         2          2.5              3
                              15
                                                                                                      µ = 0.0246
                              10

                               5

                               0
                                   0               0.5          1       1.5         2          2.5              3
                              15

                              10                                                                      µ = 0.0241
                               5

                               0
                                   0               0.5          1       1.5         2          2.5              3
                              15
                                                                                                       µ=   0.595
                              10

                               5

                               0
                                   0               0.5          1       1.5         2          2.5              3




                             0.5
                                                                              VV
                                                                       HH
                             0.4


                             0.3
                        κ2




                             0.2
                                                     VH
                                                     HV
                                                                                                      co−pol test
                             0.1                                                                       x−pol test
                                               0
                                       K       G
                                                                                                      scalar test
                              0
                                           W

                                           0             0.05   0.1   0.15    0.2       0.25    0.3         0.35
                                                                         κ3




              15 / 17
Mixtures Give Multi-texture
Example: small co-pol difference, large cross-pol difference.
Texture (skewness) of each mixture are different = Multi-texture.




                              16 / 17
Conclusions

 • Developed a Multi-texture segmentation algorithm

 • Tests found only the Scalar-texture case

 • Previous window-estimation may have found multi-texture
   due to mixtures

 • This automatic segmentation algorithm will split-up such
   mixtures

 • Less complicated scalar-product model is generally suitable
   of PolSAR analysis

Wanted: Data-sets that may display multi-texture for testing.
                              17 / 17

More Related Content

What's hot

Generalized Nonlinear Models in R
Generalized Nonlinear Models in RGeneralized Nonlinear Models in R
Generalized Nonlinear Models in Rhtstatistics
 
Nonlinear Stochastic Optimization by the Monte-Carlo Method
Nonlinear Stochastic Optimization by the Monte-Carlo MethodNonlinear Stochastic Optimization by the Monte-Carlo Method
Nonlinear Stochastic Optimization by the Monte-Carlo MethodSSA KPI
 
A current perspectives of corrected operator splitting (os) for systems
A current perspectives of corrected operator splitting (os) for systemsA current perspectives of corrected operator splitting (os) for systems
A current perspectives of corrected operator splitting (os) for systemsAlexander Decker
 
Hybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity MarketHybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity Markettomoyukiichiba
 
A four dimensional analysis of agricultural data
A four dimensional analysis of agricultural dataA four dimensional analysis of agricultural data
A four dimensional analysis of agricultural dataMargaret Donald
 
Lecture 02 internet video search
Lecture 02 internet video searchLecture 02 internet video search
Lecture 02 internet video searchzukun
 
Likelihood survey-nber-0713101
Likelihood survey-nber-0713101Likelihood survey-nber-0713101
Likelihood survey-nber-0713101NBER
 
Monte Carlo Statistical Methods
Monte Carlo Statistical MethodsMonte Carlo Statistical Methods
Monte Carlo Statistical MethodsChristian Robert
 
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...zukun
 
Jyokyo-kai-20120605
Jyokyo-kai-20120605Jyokyo-kai-20120605
Jyokyo-kai-20120605ketanaka
 
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...Hiroyuki KASAI
 
Lecture5 limit
Lecture5 limitLecture5 limit
Lecture5 limitEhsan San
 

What's hot (20)

Generalized Nonlinear Models in R
Generalized Nonlinear Models in RGeneralized Nonlinear Models in R
Generalized Nonlinear Models in R
 
Nonlinear Stochastic Optimization by the Monte-Carlo Method
Nonlinear Stochastic Optimization by the Monte-Carlo MethodNonlinear Stochastic Optimization by the Monte-Carlo Method
Nonlinear Stochastic Optimization by the Monte-Carlo Method
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
A current perspectives of corrected operator splitting (os) for systems
A current perspectives of corrected operator splitting (os) for systemsA current perspectives of corrected operator splitting (os) for systems
A current perspectives of corrected operator splitting (os) for systems
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
Hybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity MarketHybrid Atlas Models of Financial Equity Market
Hybrid Atlas Models of Financial Equity Market
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
A four dimensional analysis of agricultural data
A four dimensional analysis of agricultural dataA four dimensional analysis of agricultural data
A four dimensional analysis of agricultural data
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
Lecture 02 internet video search
Lecture 02 internet video searchLecture 02 internet video search
Lecture 02 internet video search
 
Cpsc125 ch6sec3
Cpsc125 ch6sec3Cpsc125 ch6sec3
Cpsc125 ch6sec3
 
Likelihood survey-nber-0713101
Likelihood survey-nber-0713101Likelihood survey-nber-0713101
Likelihood survey-nber-0713101
 
Monte Carlo Statistical Methods
Monte Carlo Statistical MethodsMonte Carlo Statistical Methods
Monte Carlo Statistical Methods
 
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
 
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...
CVPR2010: Advanced ITinCVPR in a Nutshell: part 5: Shape, Matching and Diverg...
 
Jyokyo-kai-20120605
Jyokyo-kai-20120605Jyokyo-kai-20120605
Jyokyo-kai-20120605
 
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...
ICML2016: Low-rank tensor completion: a Riemannian manifold preconditioning a...
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
Lecture5 limit
Lecture5 limitLecture5 limit
Lecture5 limit
 

Viewers also liked

EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
 
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUSGMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUSgrssieee
 
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...grssieee
 
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...grssieee
 
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...grssieee
 
3_Terrain catergorization for single Pol.ppt
3_Terrain catergorization for single Pol.ppt3_Terrain catergorization for single Pol.ppt
3_Terrain catergorization for single Pol.pptgrssieee
 
POLSAR CHANGE DETECTION
POLSAR CHANGE DETECTIONPOLSAR CHANGE DETECTION
POLSAR CHANGE DETECTIONsiufu
 
Madeira (Resistência dos materiais)
Madeira (Resistência dos materiais)Madeira (Resistência dos materiais)
Madeira (Resistência dos materiais)Lais Ferraz
 
Apostilinha de estruturas
Apostilinha de estruturasApostilinha de estruturas
Apostilinha de estruturasrcarc
 
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAIS
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAISFundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAIS
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAISguidify
 
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...grssieee
 
Pré dimensionamento estrutural
Pré dimensionamento estruturalPré dimensionamento estrutural
Pré dimensionamento estruturalCarlos Elson Cunha
 

Viewers also liked (14)

EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUSGMES SPACE COMPONENT:PROGRAMMATIC STATUS
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
 
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
 
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...
NOISE-ROBUST SPATIAL PREPROCESSING PRIOR TO ENDMEMBER EXTRACTION FROM HYPERSP...
 
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
 
3_Terrain catergorization for single Pol.ppt
3_Terrain catergorization for single Pol.ppt3_Terrain catergorization for single Pol.ppt
3_Terrain catergorization for single Pol.ppt
 
POLSAR CHANGE DETECTION
POLSAR CHANGE DETECTIONPOLSAR CHANGE DETECTION
POLSAR CHANGE DETECTION
 
Estruturas de madeira aula
Estruturas de madeira aulaEstruturas de madeira aula
Estruturas de madeira aula
 
Madeira (Resistência dos materiais)
Madeira (Resistência dos materiais)Madeira (Resistência dos materiais)
Madeira (Resistência dos materiais)
 
Apostilinha de estruturas
Apostilinha de estruturasApostilinha de estruturas
Apostilinha de estruturas
 
Aula 4 vigas
Aula 4   vigasAula 4   vigas
Aula 4 vigas
 
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAIS
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAISFundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAIS
Fundações - REPRESENTAÇÃO GRÁFICA DE ELEMENTOS ESTRUTURAIS
 
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
 
Pré dimensionamento estrutural
Pré dimensionamento estruturalPré dimensionamento estrutural
Pré dimensionamento estrutural
 

Similar to SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL

NIPS2010: optimization algorithms in machine learning
NIPS2010: optimization algorithms in machine learningNIPS2010: optimization algorithms in machine learning
NIPS2010: optimization algorithms in machine learningzukun
 
re:mobidyc the overview
re:mobidyc the overviewre:mobidyc the overview
re:mobidyc the overviewESUG
 
New data structures and algorithms for \\post-processing large data sets and ...
New data structures and algorithms for \\post-processing large data sets and ...New data structures and algorithms for \\post-processing large data sets and ...
New data structures and algorithms for \\post-processing large data sets and ...Alexander Litvinenko
 
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALING
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALINGWeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALING
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALINGdessybudiyanti
 
exceptionaly long-range quantum lattice models
exceptionaly long-range quantum lattice modelsexceptionaly long-range quantum lattice models
exceptionaly long-range quantum lattice modelsMauritz van den Worm
 
Data sparse approximation of the Karhunen-Loeve expansion
Data sparse approximation of the Karhunen-Loeve expansionData sparse approximation of the Karhunen-Loeve expansion
Data sparse approximation of the Karhunen-Loeve expansionAlexander Litvinenko
 
Data sparse approximation of Karhunen-Loeve Expansion
Data sparse approximation of Karhunen-Loeve ExpansionData sparse approximation of Karhunen-Loeve Expansion
Data sparse approximation of Karhunen-Loeve ExpansionAlexander Litvinenko
 
Uncertain Volatility Models
Uncertain Volatility ModelsUncertain Volatility Models
Uncertain Volatility ModelsSwati Mital
 
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...Alexander Litvinenko
 
Tpr star tree
Tpr star treeTpr star tree
Tpr star treeWin Yu
 
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQL
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQLModeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQL
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQLKostis Kyzirakos
 
11.the comparative study of finite difference method and monte carlo method f...
11.the comparative study of finite difference method and monte carlo method f...11.the comparative study of finite difference method and monte carlo method f...
11.the comparative study of finite difference method and monte carlo method f...Alexander Decker
 
The comparative study of finite difference method and monte carlo method for ...
The comparative study of finite difference method and monte carlo method for ...The comparative study of finite difference method and monte carlo method for ...
The comparative study of finite difference method and monte carlo method for ...Alexander Decker
 
VahidAkbariTalk.pdf
VahidAkbariTalk.pdfVahidAkbariTalk.pdf
VahidAkbariTalk.pdfgrssieee
 
VahidAkbariTalk_v3.pdf
VahidAkbariTalk_v3.pdfVahidAkbariTalk_v3.pdf
VahidAkbariTalk_v3.pdfgrssieee
 
Condition Monitoring Of Unsteadily Operating Equipment
Condition Monitoring Of Unsteadily Operating EquipmentCondition Monitoring Of Unsteadily Operating Equipment
Condition Monitoring Of Unsteadily Operating EquipmentJordan McBain
 

Similar to SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL (20)

NIPS2010: optimization algorithms in machine learning
NIPS2010: optimization algorithms in machine learningNIPS2010: optimization algorithms in machine learning
NIPS2010: optimization algorithms in machine learning
 
re:mobidyc the overview
re:mobidyc the overviewre:mobidyc the overview
re:mobidyc the overview
 
New data structures and algorithms for \\post-processing large data sets and ...
New data structures and algorithms for \\post-processing large data sets and ...New data structures and algorithms for \\post-processing large data sets and ...
New data structures and algorithms for \\post-processing large data sets and ...
 
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALING
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALINGWeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALING
WeightedWEIGHTED METRIC MULTIDIMENSIONAL SCALING
 
exceptionaly long-range quantum lattice models
exceptionaly long-range quantum lattice modelsexceptionaly long-range quantum lattice models
exceptionaly long-range quantum lattice models
 
Data sparse approximation of the Karhunen-Loeve expansion
Data sparse approximation of the Karhunen-Loeve expansionData sparse approximation of the Karhunen-Loeve expansion
Data sparse approximation of the Karhunen-Loeve expansion
 
Slides
SlidesSlides
Slides
 
Data sparse approximation of Karhunen-Loeve Expansion
Data sparse approximation of Karhunen-Loeve ExpansionData sparse approximation of Karhunen-Loeve Expansion
Data sparse approximation of Karhunen-Loeve Expansion
 
Uncertain Volatility Models
Uncertain Volatility ModelsUncertain Volatility Models
Uncertain Volatility Models
 
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...
Possible applications of low-rank tensors in statistics and UQ (my talk in Bo...
 
Crystallite size nanomaterials
Crystallite size   nanomaterialsCrystallite size   nanomaterials
Crystallite size nanomaterials
 
ACM 2013-02-25
ACM 2013-02-25ACM 2013-02-25
ACM 2013-02-25
 
Tpr star tree
Tpr star treeTpr star tree
Tpr star tree
 
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQL
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQLModeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQL
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQL
 
SPAA11
SPAA11SPAA11
SPAA11
 
11.the comparative study of finite difference method and monte carlo method f...
11.the comparative study of finite difference method and monte carlo method f...11.the comparative study of finite difference method and monte carlo method f...
11.the comparative study of finite difference method and monte carlo method f...
 
The comparative study of finite difference method and monte carlo method for ...
The comparative study of finite difference method and monte carlo method for ...The comparative study of finite difference method and monte carlo method for ...
The comparative study of finite difference method and monte carlo method for ...
 
VahidAkbariTalk.pdf
VahidAkbariTalk.pdfVahidAkbariTalk.pdf
VahidAkbariTalk.pdf
 
VahidAkbariTalk_v3.pdf
VahidAkbariTalk_v3.pdfVahidAkbariTalk_v3.pdf
VahidAkbariTalk_v3.pdf
 
Condition Monitoring Of Unsteadily Operating Equipment
Condition Monitoring Of Unsteadily Operating EquipmentCondition Monitoring Of Unsteadily Operating Equipment
Condition Monitoring Of Unsteadily Operating Equipment
 

More from grssieee

THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESTHE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESgrssieee
 
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERPROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERgrssieee
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...grssieee
 
test 34mb wo animations
test  34mb wo animationstest  34mb wo animations
test 34mb wo animationsgrssieee
 
2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdfgrssieee
 
DLR open house
DLR open houseDLR open house
DLR open housegrssieee
 
DLR open house
DLR open houseDLR open house
DLR open housegrssieee
 
DLR open house
DLR open houseDLR open house
DLR open housegrssieee
 
Tana_IGARSS2011.ppt
Tana_IGARSS2011.pptTana_IGARSS2011.ppt
Tana_IGARSS2011.pptgrssieee
 
Solaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.pptSolaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.pptgrssieee
 
Sakkas.ppt
Sakkas.pptSakkas.ppt
Sakkas.pptgrssieee
 
Lagios_et_al_IGARSS_2011.ppt
Lagios_et_al_IGARSS_2011.pptLagios_et_al_IGARSS_2011.ppt
Lagios_et_al_IGARSS_2011.pptgrssieee
 
IGARSS-GlobWetland-II_2011-07-20_v2-0.ppt
IGARSS-GlobWetland-II_2011-07-20_v2-0.pptIGARSS-GlobWetland-II_2011-07-20_v2-0.ppt
IGARSS-GlobWetland-II_2011-07-20_v2-0.pptgrssieee
 
igarss_2011_breunig_DLR_TDM_water_mask.ppt
igarss_2011_breunig_DLR_TDM_water_mask.pptigarss_2011_breunig_DLR_TDM_water_mask.ppt
igarss_2011_breunig_DLR_TDM_water_mask.pptgrssieee
 
Liwenchao.ppt
Liwenchao.pptLiwenchao.ppt
Liwenchao.pptgrssieee
 

More from grssieee (20)

THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIESTHE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
 
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETERPROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
 
Test
TestTest
Test
 
test 34mb wo animations
test  34mb wo animationstest  34mb wo animations
test 34mb wo animations
 
Test 70MB
Test 70MBTest 70MB
Test 70MB
 
Test 70MB
Test 70MBTest 70MB
Test 70MB
 
2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf2011_Fox_Tax_Worksheets.pdf
2011_Fox_Tax_Worksheets.pdf
 
DLR open house
DLR open houseDLR open house
DLR open house
 
DLR open house
DLR open houseDLR open house
DLR open house
 
DLR open house
DLR open houseDLR open house
DLR open house
 
Tana_IGARSS2011.ppt
Tana_IGARSS2011.pptTana_IGARSS2011.ppt
Tana_IGARSS2011.ppt
 
Solaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.pptSolaro_IGARSS_2011.ppt
Solaro_IGARSS_2011.ppt
 
Sakkas.ppt
Sakkas.pptSakkas.ppt
Sakkas.ppt
 
Rocca.ppt
Rocca.pptRocca.ppt
Rocca.ppt
 
Lagios_et_al_IGARSS_2011.ppt
Lagios_et_al_IGARSS_2011.pptLagios_et_al_IGARSS_2011.ppt
Lagios_et_al_IGARSS_2011.ppt
 
IGARSS-GlobWetland-II_2011-07-20_v2-0.ppt
IGARSS-GlobWetland-II_2011-07-20_v2-0.pptIGARSS-GlobWetland-II_2011-07-20_v2-0.ppt
IGARSS-GlobWetland-II_2011-07-20_v2-0.ppt
 
igarss_2011_breunig_DLR_TDM_water_mask.ppt
igarss_2011_breunig_DLR_TDM_water_mask.pptigarss_2011_breunig_DLR_TDM_water_mask.ppt
igarss_2011_breunig_DLR_TDM_water_mask.ppt
 
Liwenchao.ppt
Liwenchao.pptLiwenchao.ppt
Liwenchao.ppt
 

SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL

  • 1. FACULTY OF SCIENCE AND TECHNOLOGY DEPARTMENT OF PHYSICS AND TECHNOLOGY SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL Anthony P. Doulgeris, S. N. Anfinsen, and T. Eltoft IGARSS 2012
  • 2. Background: IGARSS 2011 Presented a multi-texture model for PolSAR data • Probability Density Function for Scalar and Dual-texture models • Log-cumulant expressions for all multi-texture models • Hypothesis tests to determine most appropriate multi-texture model Showed evidence for multi-texture from manually chosen box- window estimates 2 / 17
  • 3. Objective 2012 Place multi-texture models into an advanced segmentation algorithm • Hypothesis tests to choose between Scalar or Dual-texture models • U -distribution for flexible texture modelling • Log-cumulants for parameter estimation • Goodness-of-fit testing for number of clusters • Markov Random Fields for contextual smoothing Show real multi-texture segmentation results. 3 / 17
  • 4. Scalar-texture Scattering vector: s = [shh; shv ; svh; svv ]t Scalar product model: √ s= tx where texture variable t ∼ Γ(1, α), Γ−1(1, λ), or F(1, α, λ) c speckle variable x ∼ Nd (0, Σ) 1. Scalar t modulates all channels equally. 2. Speculation: scattering mechanisms impact specific channels and may lead to different textural characteristics per channel. 4 / 17
  • 5. Multi-texture Scattering vector: s = [shh; shv ; svh; svv ]t Multi-texture product model: s = T1/2x where T = diag{thh; thv ; tvh; tvv } Special cases: Scalar-texture t = thh = thv = tvh = tvv Dual-texture tco = thh = tvv and tcross = thv = tvh 5 / 17
  • 6. Multi-texture PDF Given s = T1/2x L 1 C = sisH = T1/2CxT1/2 i L i=1 LLd|C|L−d etr(−LΣ−1T−1/2CT−1/2) x fC|T(C|T; L, Σx) = Γd(L)|T|L|Σx|L Then fC(C; L, Σx) = fC|T(C|T; L, Σx)fT(T)dT 6 / 17
  • 7. Dual-texture Case - Reciprocal and reflection symmetric assumptions L3L |C|L−3 fC(C; L, Σx) = Γ3(L) |Σx|L 1 q11c11+q14c41+q41c14+q44c44 × t2L exp −L tco ftco (tco) dtco co 1 q22c22+q23c32+q32c23+q33c33 × t2L exp −L tcross ftcross (tcross)dtcross cross where qij denotes the ij th elements of Σ−1 s ftco (tco) and ftcross (tcross) denotes the PDFs of tco and tcross, respectively. 7 / 17
  • 8. Multi-texture Log-Cumulants Scalar: κν {C} = κν {W} + dν κν {T } Dual: κν {C} = κν {W} + dν κν {Tco} + dν κν {Tcross} co cross (Scaled) Wishart-distribution (L, Σ) (0) ψd (L) + ln |Σ| − d ln(L) , ν = 1 κν {W} = (ν−1) ψd (L) ,ν > 1 F -distribution (α, λ) ψ (0)(α) − ψ (0)(λ) + ln( λ−1 ) , ν = 1 α κν {T } = ψ (ν−1)(α) + (−1)ν ψ (ν−1)(λ) , ν > 1 Texture Parameters: Scalar (α, λ) Dual (αco, λco) & (αcross, λcross) 8 / 17
  • 9. Multi-texture Hypothesis Test Scalar: κν {C} = κν {W} + dν κν {T } Dual: κν {C} = κν {W} + dν κν {Tco} + dν κν {Tcross} co cross Estimate texture parameters for T , Tco and Tcross Choose from smallest of Dscalar or Ddual 10 o 9 8 Dscalar 7 X 6 Kappa 2 Ddual 5 o 4 3 2 1 W 0 −5 −4 −3 −2 −1 0 1 2 3 4 5 Kappa 3 9 / 17
  • 10. Segmentation Algorithm Iterative expectation maximisation algorithm with a few modifications, scalar-texture version detailed in Doulgeris et al. TGRS-EUSAR (2011) and Doulgeris et al. EUSAR (2012). The key features are: • U -distribution for flexible texture modelling • Log-cumulants for parameter estimation • Goodness-of-fit testing for number of clusters • Markov Random Fields for contextual smoothing Hypothesis tests to choose Scalar or Dual-texture model 10 / 17
  • 11. Segmentation Algorithm → Multi-texture Iterative expectation maximisation algorithm with a few modifications, scalar-texture version detailed in Doulgeris et al. TGRS-EUSAR (2011) and Doulgeris et al. EUSAR (2012). The key features are: • U -distribution for flexible texture modelling → Multi-texture • Log-cumulants for parameter estimation → Multi-texture • Goodness-of-fit testing for number of clusters • Markov Random Fields for contextual smoothing • Hypothesis tests to choose Scalar or Dual-texture model 10 / 17
  • 12. Simulated 8-look Data K-Wishart U-distribution Class 1 α = 15 α = 16.5, λ = 4220 Co-pol K-Wishart α = 10 α = 10.4, λ = 217 Class 2 Cross-pol G 0 λ = 30 α = 4220, λ = 28.8 Lexicographic RGB 11 / 17
  • 13. Simulated Results (a) Lexicographic RGB (b) Class segmentation (d) Class log-cumulants (c) Class histograms (e)Dual-texture log-cumulants 12 / 17
  • 14. Real Data 1: San Francisco City Radarsat-2 sample image from 9 April, 2008, 25-looks. 13 / 17
  • 15. Real Data 2: Amazon Rainforest ALOS PALSAR sample data from 13 March, 2007, 32-looks. 14 / 17
  • 16. NO MULTI-TEXTURE ! But previously ... 15 10 5 µ= 0.881 0 0 0.5 1 1.5 2 2.5 3 15 µ = 0.0246 10 5 0 0 0.5 1 1.5 2 2.5 3 15 10 µ = 0.0241 5 0 0 0.5 1 1.5 2 2.5 3 15 µ= 0.595 10 5 0 0 0.5 1 1.5 2 2.5 3 0.5 VV HH 0.4 0.3 κ2 0.2 VH HV co−pol test 0.1 x−pol test 0 K G scalar test 0 W 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 κ3 15 / 17
  • 17. Mixtures Give Multi-texture Example: small co-pol difference, large cross-pol difference. Texture (skewness) of each mixture are different = Multi-texture. 16 / 17
  • 18. Conclusions • Developed a Multi-texture segmentation algorithm • Tests found only the Scalar-texture case • Previous window-estimation may have found multi-texture due to mixtures • This automatic segmentation algorithm will split-up such mixtures • Less complicated scalar-product model is generally suitable of PolSAR analysis Wanted: Data-sets that may display multi-texture for testing. 17 / 17