WE3.L09 - POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITH COVARIANCE MATRIX INVARIANTS
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WE3.L09 - POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITH COVARIANCE MATRIX INVARIANTS

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WE3.L09 - POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITH COVARIANCE MATRIX INVARIANTS WE3.L09 - POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITH COVARIANCE MATRIX INVARIANTS Presentation Transcript

  • POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITHCOVARIANCE MATRIX INVARIANTS
    Jaan Praks, Aalto University, Finland
    Elise Colin Koeniguer, ONERA, France
    Martti Hallikainen, Aalto University, Finland
  • Polarimatric SAR images are often presented as false color images. Many approaches is used. Simple RGB color composite image of three polarimetric channel amplitudes is used most often, but its has some limitations.
    How colors should be used to achieve easiliy interpretative polarimetric SAR image for image browsing and quick interpretation?
    In this study we try to give some hints, how good visualisation results can be achieved.
    Introduction
    Jaan Praks
    Elise Colin Koeniguer
    Martti Hallikainen
  • To represent any numerical data in colors, we need acolor model. The color model converts numbers to colors and vice a versa.
    A brief history of Color Models
    1610 SigfridusAronusForsius (basic colors and color wheel)
    1666 Isaac Newton, Color wheel
    1766 Moses Harris adds color shades
    1810 Philipp-Otto Runge
    Thomas Young and H. Helmholtz - thrichromatic vision
    1855 J. C. Maxwell – color matching functions
    1931 CIE color system
    Color Modelshistory
  • To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.
    A brief history of Color Models
    1610 SigfridusAronusForsius (basic colors and color wheel)
    1666 Isaac Newton, Color wheel
    1766 Moses Harris adds color shades
    1810 Philipp-Otto Runge
    Thomas Young and H. Helmholtz - thrichromatic vision
    1855 J. C. Maxwell – color matching functions
    1931 CIE color system
    Color Modelshistory
  • To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.
    A brief history of Color Models
    1610 SigfridusAronusForsius (basic colors and color wheel)
    1666 Isaac Newton, Color wheel
    1766 Moses Harris adds color shades
    1810 Philipp-Otto Runge
    Thomas Young and H. Helmholtz - thrichromatic vision
    1855 J. C. Maxwell – color matching functions
    1931 CIE color system
    Color Modelshistory
  • To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.
    A brief history of Color Models
    1610 SigfridusAronusForsius (basic colors and color wheel)
    1666 Isaac Newton, Color wheel
    1766 Moses Harris adds color shades
    1810 Philipp-Otto Runge
    Thomas Young and H. Helmholtz - thrichromatic vision
    1855 J. C. Maxwell – color matching functions
    1931 CIE color system
    Color Modelshistory
  • The color models can be divided roughly into two categories:
    The parameter triplet is based on direct sensory input values. Red Green Blue (RGB) values in Cartesian coordinates, parameters are linear.
    The parameter triplet tries to mimic human comprehension of colors. HSV, HSI, HSL. Uses cylindrical or spherical coordinate system. Parameters like hue, saturation, lightness etc. Some parameters are periodic. Perceptual parameters are also often interconnected (if the saturation is zero, the hue has no values).
    Color ModelsRGB and HSV
    Images by SharkD
  • y
    x
    z
    E
    Human eye does not sense polarization, and therefore our understanding of polarization is based on abstract models
    There are many ways to visualize polarization
    • Stokes parameters
    • Polarization ellipse
    • Poincare sphere
    Polarimetric measurement systems like SAR
    • Target polarimetric signature
    Visualisingpolarization
  • Similarities between color- and polarization models
    Poincaré sphere and Runge color sphere are similar
    Both use spherical coordinates, and periodic parameters
    Fully polarized radiation can be presented in colors unambiguously
    Philipp-Otto Runge color sphere
    Poincare polarization sphere
  • Maxwell color triangle and ternary use the same technique to represent three variables restricted by constant sum a + b + c = const
    Situation where a = b = c has a special meaning (white color)
    In polarimetry, we have similar variables, for example normalized eigenvalues (scattering mechanism probability) or normalized covariance matrix diagonal elements
    Similarities between color- and polarization models
    Maxwell color triangle
    Polarimetric classification with ternary plot
  • SAR image has more dimensions than is possible to represent in color image
    - all parameters cannot be visualized in the same image
    - there will always be more than few representation approaches
    Color model should be selected according to image parameter relations and nature, periodic variables should be connected with periodic and linear parameters with linear
    Cylindrical color systems often provide easier way to separate different parameters for independent processing
    (P. Imbo, J.C. Souyris, A. Lopes, and P. Marthon, “Synoptic representation of the polarimetric information,” in Proceedings CEOS SAR Workshop, Toulouse, France, October26-29 1999.)
    Some remarks on polarimetric SAR image color representation
  • Parameter scaling and histogram equalization changes often also interpretation
    Intensity of the SAR image is similar to optical image - intensity is easy to treat separately
    In some color systems, different parameters can have different resolution
    Parameter relations should be taken into account
    - equal RGB give white color and it should have physical meaning in visualization
    - saturation affects also hue parameter
    Some remarks on polarimetric SAR image color representation
  • Different parameter layers can have different resolution in HSl type of color models without effect to interpretation.
    This can be utilized in synoptic representation where depolarization related parameters are calculated for averaged image.
    ExamplesLayers with different resolution
  • Z-4
    Z-7
    Z-4
    Z-7
    Z-5
    Z-5
    Z-8
    Z-8
    Z-6
    Z-6
    Z-9
    Z-9
    Hue - hi-resalphaSaturation - entropyIntensity - hi-resspan
    Hue - alphaSaturation - entropyIntensity - span
  • When histogram of the parameter is narrow, parameter image has low dynamics. Histogram equalization gives usually visually pleasing results.
    However, histogram manipulation can change also interpretation and/or physical relation between the parameters, if parameters are related.
    Examplesparameter scaling
  • NicolasTrouve, Preliminary Polarimetric Segmentation on SAR images. EUSAR 2010.
  • In addition to intensity, polarimetric parameters can be divided roughly into three classes
    1. Parameters connected to phase change of eigenpolarization states
    - often periodic variables, suitable for hue
    2. Parameters connected to change of amplitude
    - periodic and linear parameters
    3. Parameters connected depolarization properties
    - controls also other polarimetric parameters, suitable for saturation
    E. Colin-Koeniguer, N. Trouvé, J. Praks ”A review about alternatives to classical Polarimetric SAR parameters”, EUSAR 2010
    Classes of polarimetric parameters
  • Normalizedeigenvalues in trenaryplot
  • Normalizedeigenvalues in trenaryplot, modulatedwithintensity
  • Covariancematrix and normalizedcovariancematrix
    Surfacescatteringfraction (similar to alpha angle)
    Scatteringdiversity (similar to entropy)
    NormalizedcovarianceMatrixinvariants
    Praks, Jaan; Koeniguer, Elise Colin; Hallikainen, Martti T., Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry, IEEE Trans. Geoscience Rem. Sens., vol. 47, issue 7, pp. 2262-2274
  • Normalized Pauli components (N main diagonal)
  • Normalized Pauli componentswithintensity
  • Intensity, scatteringdiveristy and surfacescatteringfraction in synopticrepresentation
  • When visualizing SAR images with colors, special attention should be paid to color model selection for given parameters
    Well selected color model and visualization scheme can be in the best case self explaining
    Visualization scheme can explain also physical meaning of selected parameters
    Parameter scaling for SAR images should be done with care and consideration, in order not to loose physical interpretation
    Normalized covariance matrix provides suitable parameters for several good visualization schemes
    Conclusions
  • Thankyou!