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110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
110725-29_IGARSS_Ferro_04.pptx
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110725-29_IGARSS_Ferro_04.pptx

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  • Ricordare di dierorbitingmaininstruments
  • Dati non elaboratipuressendodisponibili perché molti.
  • Citare valore medio dell’erroretotale.Aggiungere la media in fondo.Globalmente le prestazionisonomoltobuone.
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    • 1. A Novel Approach to the Automatic Detection of Subsurface Features in Planetary Radar Sounder Signals<br />Adamo Ferro<br />Lorenzo Bruzzone<br />E-mail: adamo.ferro@disi.unitn.it<br />Web page: http://rslab.disi.unitn.it<br />
    • 2. 2<br />A. Ferro, L. Bruzzone<br />Outline<br />Introduction<br />1<br />Aim of the Work<br />2<br />Statistical Analysis of Radar Sounder Signals<br />3<br />Automatic Detection of Basal Returns<br />4<br />Conclusions and Future Work<br />5<br />
    • 3. Introduction<br />3<br />A. Ferro, L. Bruzzone<br /><ul><li>Planetary radar sounders can probe the subsurface of the target body from orbit.
    • 4. Main instruments:
    • 5. Moon: ALSE and LRS
    • 6. Mars: MARSIS and SHARAD
    • 7. Their effectiveness lead to the proposal of new orbiting radar sounders, also for Earth science:
    • 8. IPR and SSR for the Jovian Moons[1]
    • 9. GLACIES proposal for the Earth[2]
    • 10. Radar sounder data have been analyzed mostly by means of manual investigations.</li></ul>v<br />Platform height<br />Nadir<br />Across track<br />Range (depth)<br />[1] L. Bruzzone, G. Alberti, C. Catallo, A. Ferro, W. Kofman, and R. Orosei, “Sub-surface radar sounding of the Jovian moon Ganymede,” Proceedings of the IEEE, 2011.<br />[2] L. Bruzzone et al., “GLACiers and Icy Environments Sounding ,” response to ESA’s EE-8 call, 2010.<br />Example of radargram (SHARAD)<br />
    • 11. State of the Art<br />4<br />A. Ferro, L. Bruzzone<br /><ul><li>Past works related to the automatic analysis of radar sounder data regard the analysis of ground-based or airborne GPR signals.
    • 12. Different frequency ranges.
    • 13. Better spatial resolution.
    • 14. Detection of buried objects (e.g., mines, pipes) which show specific signatures (e.g., hyperbolas).
    • 15. Investigation of local targets vs. regional and global mapping.
    • 16. Planetary radar sounding missions are providing a very large amount of data.
    • 17. In order to effectively extract information from such data automatic techniques can greatly support scientists’ work.</li></li></ul><li>ProposedProcessing Framework<br />5<br />A. Ferro, L. Bruzzone<br />Ground processing<br />Preprocessing<br />Information extraction<br />Level 2 products<br />Level 3 products<br />Map of interesting areas<br />Labels<br />Raw data<br />Level 1products<br />Icy layers position<br />3D tomography of icy layers<br />Basal returns position<br />Ice thickness map<br />...<br />...<br />...<br />Other inputs(e.g., ancillary data, clutter simulations)<br />
    • 18. Aim of the Work<br /><ul><li>Development of a processing framework for the automatic analysis of radar sounder data.
    • 19. Statistical analysis of radar sounder signals.
    • 20. Characterization of subsurface features.
    • 21. Basis for the development of automatic techniques for the detection of subsurface features.
    • 22. Automatic information extraction from radargrams.
    • 23. First return.
    • 24. Basal returns.
    • 25. Subsurface layering.
    • 26. Discrimination of surface clutter.</li></ul>6<br />A. Ferro, L. Bruzzone<br />
    • 27. Aim of the Work<br /><ul><li>Development of a processing framework for the automatic analysis of radar sounder data.
    • 28. Statistical analysis of radar sounder signals.
    • 29. Characterization of subsurface features.
    • 30. Basis for the development of automatic techniques for the detection of subsurface features.
    • 31. Automatic information extraction from radargrams.
    • 32. First return.
    • 33. Basal returns.
    • 34. Subsurface layering.
    • 35. Discrimination of surface clutter.</li></ul>7<br />A. Ferro, L. Bruzzone<br />
    • 36. Dataset Description<br /><ul><li>SHARAD radargrams
    • 37. Number of radargrams: 7
    • 38. Area of interest: North Polar Layered Deposits (NPLD) of Mars
    • 39. Resolution: 300 × 3000 × 15 m (along-track × across-track × range)</li></ul>8<br />A. Ferro, L. Bruzzone<br />-2500 m<br />SHARAD radargram 1319502<br />-5500 m<br />
    • 40. Proposed Approach: Statistical Analysis<br /><ul><li>Definition of targets:
    • 41. NT: no target
    • 42. SL: strong layers
    • 43. WL: weak layers
    • 44. LR: low returns
    • 45. BR: basal returns</li></ul>9<br />A. Ferro, L. Bruzzone<br /><ul><li>Goal:</li></ul>Understand the statistical properties of the amplitude distribution underlying the scattering from different target classes.<br />SHARAD radargram 1319502<br />
    • 46. Proposed Approach: Statistical Analysis<br /><ul><li>Tested statistical distributions (amplitude domain):
    • 47. Rayleigh: simplest model, scattering from a large set of scatterers with the same size.
    • 48. Nakagami: amplitude version of the Gamma distribution, has the Rayleigh has a particular case.
    • 49. K: models the scattering from scatterers not homogeneously distributed in space, which number is a negative binomial random variable.
    • 50. Distribution fitting performed via a Maximum Likelihood approach.
    • 51. Goodness of fit tested by calculating the RMSE and the Kullback-Leibler distance (KL) between the target histogram and the fitted distribution.</li></ul>10<br />A. Ferro, L. Bruzzone<br />Mean power<br />Amplitude<br />Shapeparameter<br />Shapeparameter<br />
    • 52. Proposed Approach: Statistical Analysis, Fitting<br />11<br />A. Ferro, L. Bruzzone<br />SHARAD radargram 1319502<br />No target<br />Weak layers<br />Strong layers<br />Low returns<br />Basal returns<br />Summary<br />
    • 53. Results: Statistical Analysis<br /><ul><li>Best fitting distribution: K distribution
    • 54. The parameters of the distribution describe statistically the characteristics of the target.
    • 55. Noise can be modeled with a simple Rayleigh distribution.</li></ul>12<br />A. Ferro, L. Bruzzone<br />
    • 56. 13<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br />Inputradargram<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />BR map<br />
    • 57. 14<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>Frame-based detection of the first return.
    • 58. Map of the KLHN:
    • 59. Calculated for the subsurface area using a sliding window approach.
    • 60. It represents a meta-level between the amplitude data and the final product.</li></ul>Inputradargram<br />Local histogram<br />Estimatednoisedistribution<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />SHARAD radargram 1319502<br />BR map<br />
    • 61. 15<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>Frame-based detection of the first return.
    • 62. Map of the KLHN:
    • 63. Calculated for the subsurface area using a sliding window approach.
    • 64. It represents a meta-level between the amplitude data and the final product.</li></ul>Inputradargram<br />Local histogram<br />Estimatednoisedistribution<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />SHARAD radargram 1319502<br />BR map<br />
    • 65. 16<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>Frame-based detection of the first return.
    • 66. Map of the KLHN:
    • 67. Calculated for the subsurface area using a sliding window approach.
    • 68. It represents a meta-level between the amplitude data and the final product.</li></ul>Inputradargram<br />Local histogram<br />Estimatednoisedistribution<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />SHARAD radargram 1319502<br />KLHN map<br />BR map<br />
    • 69. 17<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>Selection of the regions with the highest probability to be related to the basal scattering area.
    • 70. The initial BR map is created using a region growing approach based on level sets which starts from the seeds and moves on the KLHN map.</li></ul>Inputradargram<br />Propagation<br />Curvature<br />Level set function<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />KLHN map<br />Initial BR map<br />BR map<br />
    • 71. 18<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples.
    • 72. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    • 73. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.</li></ul>Inputradargram<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />Initial BR map<br />BR map<br />
    • 74. 19<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples.
    • 75. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    • 76. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.</li></ul>Inputradargram<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />Step 2<br />BR map<br />
    • 77. 20<br />A. Ferro, L. Bruzzone<br />First return detection<br />Calculation of KLHN<br />Thresholding<br />BR seed selection<br />Region growing<br />for m=2 to M<br />Estimation of BR statistics<br />Thresholding<br />BR seed selection<br />Region growing<br />Region selection<br />BR map generation<br />Proposed Approach: Automatic Detection of BR<br /><ul><li>The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples.
    • 78. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    • 79. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.</li></ul>Inputradargram<br />KLHN map<br />Initial BR map<br />KL1<br />KLm<br />Step 3<br />BR map<br />
    • 80. Results: Automatic Detection of BR<br />21<br />A. Ferro, L. Bruzzone<br />SHARAD radargram 1319502<br />SHARAD radargram 0371502<br />SHARAD radargram 1292401<br />SHARAD radargram 1312901<br />
    • 81. 22<br />A. Ferro, L. Bruzzone<br />Results: Automatic Detection of BR<br /><ul><li>The performance of the technique has been measured quantitatively.
    • 82. Selection of 3000 reference samples randomly taken in areas of the radargram where BR returns are (or are not) visible.
    • 83. Counted the number of samples correctly detected as BR (or not BR) returns.</li></li></ul><li>Results: LayerDensityEstimation<br />23<br />A. Ferro, L. Bruzzone<br />SHARAD radargram 052052<br />Automatic detection of linear interfaces<br />Interface density map<br />
    • 84. Conclusions<br />24<br />A. Ferro, L. Bruzzone<br /><ul><li>Developing a processing framework for the analysis of radar sounder data.
    • 85. Statistical analysis of radar sounder signals.
    • 86. It can support the analysis of the radargrams.
    • 87. Different statistics / different targets.
    • 88. Generation of statisticalmapsusefulto drive detectionalgorithms.
    • 89. Novel technique for the automatic detection of the basal returns from radar sounder data using statistical techniques.
    • 90. Effectively tested on SHARAD radargrams.
    • 91. Possible applications: estimation of ice thickness, detection of local buried basins or impact craters, 3D measurement of the scattered power, study seasonal variation of the signal loss through the ice.</li></li></ul><li>Future Work<br />25<br />A. Ferro, L. Bruzzone<br /><ul><li>Improvements of the proposed technique:
    • 92. Estimation of local statistics using context-sensitive techniques for the adaptive determination of the local parcel size.
    • 93. Develop a procedure for the automatic and adaptive definition of the parameters of the proposed technique.
    • 94. Adapt the algorithm to airborne acquisitions on Earth’s Poles.
    • 95. Other possible developments:
    • 96. Integration of the automatic detection of linear interfaces and basal returns to higher level products.
    • 97. Automatic detection and filtering of surface clutter returns from the radargrams.</li></li></ul><li>26<br />A. Ferro, L. Bruzzone<br />Thank you for your attention!<br />Contacts:<br /><ul><li>E-mail: adamo.ferro@disi.unitn.it
    • 98. Website: http://rslab.disi.unitn.it</li></li></ul><li>27<br />A. Ferro, L. Bruzzone<br />BACKUPSLIDES<br />
    • 99. AutomaticDetection of SurfaceClutter, Example<br />28<br />A. Ferro, L. Bruzzone<br />SHARAD radargram 1386001<br />Coregistered surface clutter simulation<br />Detected surface clutter map<br />
    • 100. Automatic Detection of the NPLD BR, Results<br />29<br />A. Ferro, L. Bruzzone<br />Example of application to a large number of tracks<br />-2300<br />20<br />-4000<br />0<br />Depth of detected BR fromdetected surface return [µs]<br />Coverage of selected 45 tracks<br />Mars North Pole topography [m]<br />180º<br />90º<br />270º<br />88º<br />86º<br />84º<br />82º<br />0º<br />
    • 101. Results: Automatic Detection of BR<br />30<br />A. Ferro, L. Bruzzone<br />SHARAD radargram 1319502<br />SHARAD radargram 0371502<br />SHARAD radargram 1292401<br />SHARAD radargram 1312901<br />
    • 102. Modelparameters<br />31<br />A. Ferro, L. Bruzzone<br />

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