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.
  • 110725-29_IGARSS_Ferro_04.pptx

    1. 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. 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. 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. 4. Main instruments:
    5. 5. Moon: ALSE and LRS
    6. 6. Mars: MARSIS and SHARAD
    7. 7. Their effectiveness lead to the proposal of new orbiting radar sounders, also for Earth science:
    8. 8. IPR and SSR for the Jovian Moons[1]
    9. 9. GLACIES proposal for the Earth[2]
    10. 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. 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. 12. Different frequency ranges.
    13. 13. Better spatial resolution.
    14. 14. Detection of buried objects (e.g., mines, pipes) which show specific signatures (e.g., hyperbolas).
    15. 15. Investigation of local targets vs. regional and global mapping.
    16. 16. Planetary radar sounding missions are providing a very large amount of data.
    17. 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. 18. Aim of the Work<br /><ul><li>Development of a processing framework for the automatic analysis of radar sounder data.
    19. 19. Statistical analysis of radar sounder signals.
    20. 20. Characterization of subsurface features.
    21. 21. Basis for the development of automatic techniques for the detection of subsurface features.
    22. 22. Automatic information extraction from radargrams.
    23. 23. First return.
    24. 24. Basal returns.
    25. 25. Subsurface layering.
    26. 26. Discrimination of surface clutter.</li></ul>6<br />A. Ferro, L. Bruzzone<br />
    27. 27. Aim of the Work<br /><ul><li>Development of a processing framework for the automatic analysis of radar sounder data.
    28. 28. Statistical analysis of radar sounder signals.
    29. 29. Characterization of subsurface features.
    30. 30. Basis for the development of automatic techniques for the detection of subsurface features.
    31. 31. Automatic information extraction from radargrams.
    32. 32. First return.
    33. 33. Basal returns.
    34. 34. Subsurface layering.
    35. 35. Discrimination of surface clutter.</li></ul>7<br />A. Ferro, L. Bruzzone<br />
    36. 36. Dataset Description<br /><ul><li>SHARAD radargrams
    37. 37. Number of radargrams: 7
    38. 38. Area of interest: North Polar Layered Deposits (NPLD) of Mars
    39. 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. 40. Proposed Approach: Statistical Analysis<br /><ul><li>Definition of targets:
    41. 41. NT: no target
    42. 42. SL: strong layers
    43. 43. WL: weak layers
    44. 44. LR: low returns
    45. 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. 46. Proposed Approach: Statistical Analysis<br /><ul><li>Tested statistical distributions (amplitude domain):
    47. 47. Rayleigh: simplest model, scattering from a large set of scatterers with the same size.
    48. 48. Nakagami: amplitude version of the Gamma distribution, has the Rayleigh has a particular case.
    49. 49. K: models the scattering from scatterers not homogeneously distributed in space, which number is a negative binomial random variable.
    50. 50. Distribution fitting performed via a Maximum Likelihood approach.
    51. 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. 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. 53. Results: Statistical Analysis<br /><ul><li>Best fitting distribution: K distribution
    54. 54. The parameters of the distribution describe statistically the characteristics of the target.
    55. 55. Noise can be modeled with a simple Rayleigh distribution.</li></ul>12<br />A. Ferro, L. Bruzzone<br />
    56. 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. 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. 58. Map of the KLHN:
    59. 59. Calculated for the subsurface area using a sliding window approach.
    60. 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. 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. 62. Map of the KLHN:
    63. 63. Calculated for the subsurface area using a sliding window approach.
    64. 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. 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. 66. Map of the KLHN:
    67. 67. Calculated for the subsurface area using a sliding window approach.
    68. 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. 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. 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. 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. 72. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    73. 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. 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. 75. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    76. 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. 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. 78. The procedure is repeated iteratively using lower threshold ranges for the KLHN map.
    79. 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. 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. 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. 82. Selection of 3000 reference samples randomly taken in areas of the radargram where BR returns are (or are not) visible.
    83. 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. 84. Conclusions<br />24<br />A. Ferro, L. Bruzzone<br /><ul><li>Developing a processing framework for the analysis of radar sounder data.
    85. 85. Statistical analysis of radar sounder signals.
    86. 86. It can support the analysis of the radargrams.
    87. 87. Different statistics / different targets.
    88. 88. Generation of statisticalmapsusefulto drive detectionalgorithms.
    89. 89. Novel technique for the automatic detection of the basal returns from radar sounder data using statistical techniques.
    90. 90. Effectively tested on SHARAD radargrams.
    91. 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. 92. Estimation of local statistics using context-sensitive techniques for the adaptive determination of the local parcel size.
    93. 93. Develop a procedure for the automatic and adaptive definition of the parameters of the proposed technique.
    94. 94. Adapt the algorithm to airborne acquisitions on Earth’s Poles.
    95. 95. Other possible developments:
    96. 96. Integration of the automatic detection of linear interfaces and basal returns to higher level products.
    97. 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. 98. Website: http://rslab.disi.unitn.it</li></li></ul><li>27<br />A. Ferro, L. Bruzzone<br />BACKUPSLIDES<br />
    99. 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. 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. 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. 102. Modelparameters<br />31<br />A. Ferro, L. Bruzzone<br />

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