POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITHCOVARIANCE MATRIX INVARIANTS<br />Jaan Praks, Aalto University...
Polarimatric SAR images are often presented as false color images.  Many approaches is used. Simple RGB color composite im...
To represent  any numerical data in colors, we need acolor model. The color model converts numbers to colors and vice a ve...
To represent  any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a v...
To represent  any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a v...
To represent  any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a v...
The color models can be divided roughly into two categories:<br />The parameter triplet is based on direct sensory input v...
y<br />x<br />z<br />E<br />Human eye does not sense polarization, and therefore our understanding of polarization is base...
Polarization ellipse
Poincare sphere</li></ul>Polarimetric measurement systems like SAR <br /><ul><li>Target polarimetric signature</li></ul>Vi...
Similarities between color- and polarization models<br />Poincaré sphere and Runge color sphere are similar<br />Both use ...
Maxwell color triangle and ternary use the same technique to represent three variables restricted by constant sum a + b + ...
SAR image has more dimensions than is possible to represent in color image <br />	 - all parameters cannot be visualized i...
Parameter scaling and histogram equalization changes often also interpretation<br />Intensity of the SAR image is similar ...
Different parameter layers can have different resolution in HSl type of color models without effect to interpretation. <br...
Z-4<br />Z-7<br />Z-4<br />Z-7<br />Z-5<br />Z-5<br />Z-8<br />Z-8<br />Z-6<br />Z-6<br />Z-9<br />Z-9<br />Hue - hi-resal...
When histogram of the parameter is narrow, parameter image has low dynamics. Histogram equalization gives usually visually...
NicolasTrouve, Preliminary Polarimetric Segmentation on SAR images. EUSAR 2010.<br />
In addition to intensity, polarimetric parameters can be divided roughly into three classes<br />1. Parameters connected t...
Normalizedeigenvalues in trenaryplot<br />
Normalizedeigenvalues in trenaryplot, modulatedwithintensity<br />
Covariancematrix and normalizedcovariancematrix<br />Surfacescatteringfraction (similar to alpha angle)<br />Scatteringdiv...
Normalized Pauli components (N main diagonal)<br />
Normalized Pauli componentswithintensity<br />
Intensity, scatteringdiveristy and surfacescatteringfraction in synopticrepresentation<br />
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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

  1. 1. POLARIMETRIC SAR IMAGE VISUALIZATION AND INTERPRETATION WITHCOVARIANCE MATRIX INVARIANTS<br />Jaan Praks, Aalto University, Finland<br />Elise Colin Koeniguer, ONERA, France<br />Martti Hallikainen, Aalto University, Finland<br />
  2. 2. 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.<br />How colors should be used to achieve easiliy interpretative polarimetric SAR image for image browsing and quick interpretation?<br />In this study we try to give some hints, how good visualisation results can be achieved.<br />Introduction<br />Jaan Praks<br />Elise Colin Koeniguer<br />Martti Hallikainen<br />
  3. 3. To represent any numerical data in colors, we need acolor model. The color model converts numbers to colors and vice a versa.<br />A brief history of Color Models<br />1610 SigfridusAronusForsius (basic colors and color wheel)<br />1666 Isaac Newton, Color wheel<br />1766 Moses Harris adds color shades<br />1810 Philipp-Otto Runge<br />Thomas Young and H. Helmholtz - thrichromatic vision<br />1855 J. C. Maxwell – color matching functions<br />1931 CIE color system<br />Color Modelshistory<br />
  4. 4. To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.<br />A brief history of Color Models<br />1610 SigfridusAronusForsius (basic colors and color wheel)<br />1666 Isaac Newton, Color wheel<br />1766 Moses Harris adds color shades<br />1810 Philipp-Otto Runge<br />Thomas Young and H. Helmholtz - thrichromatic vision<br />1855 J. C. Maxwell – color matching functions<br />1931 CIE color system<br />Color Modelshistory<br />
  5. 5. To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.<br />A brief history of Color Models<br />1610 SigfridusAronusForsius (basic colors and color wheel)<br />1666 Isaac Newton, Color wheel<br />1766 Moses Harris adds color shades<br />1810 Philipp-Otto Runge<br />Thomas Young and H. Helmholtz - thrichromatic vision<br />1855 J. C. Maxwell – color matching functions<br />1931 CIE color system<br />Color Modelshistory<br />
  6. 6. To represent any numerical data in colors, we need a color model. The color model converts numbers to colors and vice a versa.<br />A brief history of Color Models<br />1610 SigfridusAronusForsius (basic colors and color wheel)<br />1666 Isaac Newton, Color wheel<br />1766 Moses Harris adds color shades<br />1810 Philipp-Otto Runge<br />Thomas Young and H. Helmholtz - thrichromatic vision<br />1855 J. C. Maxwell – color matching functions<br />1931 CIE color system<br />Color Modelshistory<br />
  7. 7. The color models can be divided roughly into two categories:<br />The parameter triplet is based on direct sensory input values. Red Green Blue (RGB) values in Cartesian coordinates, parameters are linear.<br />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).<br />Color ModelsRGB and HSV<br />Images by SharkD<br />
  8. 8. y<br />x<br />z<br />E<br />Human eye does not sense polarization, and therefore our understanding of polarization is based on abstract models<br />There are many ways to visualize polarization<br /><ul><li>Stokes parameters
  9. 9. Polarization ellipse
  10. 10. Poincare sphere</li></ul>Polarimetric measurement systems like SAR <br /><ul><li>Target polarimetric signature</li></ul>Visualisingpolarization<br />
  11. 11. Similarities between color- and polarization models<br />Poincaré sphere and Runge color sphere are similar<br />Both use spherical coordinates, and periodic parameters <br />Fully polarized radiation can be presented in colors unambiguously<br />Philipp-Otto Runge color sphere<br />Poincare polarization sphere<br />
  12. 12. Maxwell color triangle and ternary use the same technique to represent three variables restricted by constant sum a + b + c = const<br />Situation where a = b = c has a special meaning (white color)<br />In polarimetry, we have similar variables, for example normalized eigenvalues (scattering mechanism probability) or normalized covariance matrix diagonal elements<br />Similarities between color- and polarization models<br />Maxwell color triangle<br />Polarimetric classification with ternary plot<br />
  13. 13. SAR image has more dimensions than is possible to represent in color image <br /> - all parameters cannot be visualized in the same image<br /> - there will always be more than few representation approaches<br />Color model should be selected according to image parameter relations and nature, periodic variables should be connected with periodic and linear parameters with linear<br />Cylindrical color systems often provide easier way to separate different parameters for independent processing<br />(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.)<br />Some remarks on polarimetric SAR image color representation<br />
  14. 14. Parameter scaling and histogram equalization changes often also interpretation<br />Intensity of the SAR image is similar to optical image - intensity is easy to treat separately<br />In some color systems, different parameters can have different resolution<br />Parameter relations should be taken into account<br /> - equal RGB give white color and it should have physical meaning in visualization<br /> - saturation affects also hue parameter<br />Some remarks on polarimetric SAR image color representation<br />
  15. 15. Different parameter layers can have different resolution in HSl type of color models without effect to interpretation. <br />This can be utilized in synoptic representation where depolarization related parameters are calculated for averaged image. <br />ExamplesLayers with different resolution<br />
  16. 16. Z-4<br />Z-7<br />Z-4<br />Z-7<br />Z-5<br />Z-5<br />Z-8<br />Z-8<br />Z-6<br />Z-6<br />Z-9<br />Z-9<br />Hue - hi-resalphaSaturation - entropyIntensity - hi-resspan<br />Hue - alphaSaturation - entropyIntensity - span<br />
  17. 17. When histogram of the parameter is narrow, parameter image has low dynamics. Histogram equalization gives usually visually pleasing results.<br />However, histogram manipulation can change also interpretation and/or physical relation between the parameters, if parameters are related.<br />Examplesparameter scaling<br />
  18. 18. NicolasTrouve, Preliminary Polarimetric Segmentation on SAR images. EUSAR 2010.<br />
  19. 19. In addition to intensity, polarimetric parameters can be divided roughly into three classes<br />1. Parameters connected to phase change of eigenpolarization states <br /> - often periodic variables, suitable for hue<br />2. Parameters connected to change of amplitude <br /> - periodic and linear parameters <br />3. Parameters connected depolarization properties<br /> - controls also other polarimetric parameters, suitable for saturation<br />E. Colin-Koeniguer, N. Trouvé, J. Praks ”A review about alternatives to classical Polarimetric SAR parameters”, EUSAR 2010<br />Classes of polarimetric parameters<br />
  20. 20. Normalizedeigenvalues in trenaryplot<br />
  21. 21. Normalizedeigenvalues in trenaryplot, modulatedwithintensity<br />
  22. 22. Covariancematrix and normalizedcovariancematrix<br />Surfacescatteringfraction (similar to alpha angle)<br />Scatteringdiversity (similar to entropy)<br />NormalizedcovarianceMatrixinvariants<br />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<br />
  23. 23. Normalized Pauli components (N main diagonal)<br />
  24. 24. Normalized Pauli componentswithintensity<br />
  25. 25. Intensity, scatteringdiveristy and surfacescatteringfraction in synopticrepresentation<br />
  26. 26. When visualizing SAR images with colors, special attention should be paid to color model selection for given parameters<br />Well selected color model and visualization scheme can be in the best case self explaining <br />Visualization scheme can explain also physical meaning of selected parameters<br />Parameter scaling for SAR images should be done with care and consideration, in order not to loose physical interpretation<br />Normalized covariance matrix provides suitable parameters for several good visualization schemes<br />Conclusions<br />
  27. 27. Thankyou!<br />

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