1. Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy Adamo Ferro Lorenzo Bruzzone A Novel Approach to the Automatic Detection of Subsurface Features in Planetary Radar Sounder Signals E-mail: adamo.ferro@disi.unitn.it adamo.ferro@disi.unitn.it Web page: http://rslab.disi.unitn.it http://rslab.disi.unitn.it

2. University of Trento, Italy 2 Outline A. Ferro, L. Bruzzone Introduction Aim of the Work 1 Statistical Analysis of Radar Sounder Signals 2 3 Automatic Detection of Basal Returns 4 Conclusions and Future Work 5

3. University of Trento, Italy Introduction 3A. Ferro, L. Bruzzone Planetary radar sounders can probe the subsurface of the target body from orbit. Planetary radar sounders can probe the subsurface of the target body from orbit. Main instruments: Main instruments: Moon: ALSE and LRSMoon: ALSE and LRS Mars: MARSIS and SHARADMars: MARSIS and SHARAD Their effectiveness lead to the proposal of new orbiting radar sounders, also for Earth science: Their effectiveness lead to the proposal of new orbiting radar sounders, also for Earth science: IPR and SSR for the Jovian Moons [1]IPR and SSR for the Jovian Moons [1] GLACIES proposal for the Earth [2]GLACIES proposal for the Earth [2] Radar sounder data have been analyzed mostly by means of manual investigations. Radar sounder data have been analyzed mostly by means of manual investigations. v Range (depth) Across track Platform height Nadir Example of radargram (SHARAD) [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. [2] L. Bruzzone et al., “ GLACiers and Icy Environments Sounding,” response to ESA’s EE-8 call, 2010.

4. University of Trento, Italy State of the Art 4A. Ferro, L. Bruzzone Past works related to the automatic analysis of radar sounder data regard the analysis of ground-based or airborne GPR signals. Past works related to the automatic analysis of radar sounder data regard the analysis of ground-based or airborne GPR signals. Different frequency ranges.Different frequency ranges. Better spatial resolution.Better spatial resolution. Detection of buried objects (e.g., mines, pipes) which show specific signatures (e.g., hyperbolas).Detection of buried objects (e.g., mines, pipes) which show specific signatures (e.g., hyperbolas). Investigation of local targets vs. regional and global mapping.Investigation of local targets vs. regional and global mapping. Planetary radar sounding missions are providing a very large amount of data. Planetary radar sounding missions are providing a very large amount of data. In order to effectively extract information from such data automatic techniques can greatly support scientists’ work. In order to effectively extract information from such data automatic techniques can greatly support scientists’ work.

5. University of Trento, Italy Proposed Processing Framework 5A. Ferro, L. Bruzzone Raw data Ground processing Level 1 products Preprocessing Information extraction... Level 2 products Labels Icy layers position Basal returns position... Other inputs (e.g., ancillary data, clutter simulations) Level 3 products Map of interesting areas 3D tomography of icy layers Ice thickness map...

6. University of Trento, Italy Development of a processing framework for the automatic analysis of radar sounder data. Development of a processing framework for the automatic analysis of radar sounder data. Statistical analysis of radar sounder signals. Statistical analysis of radar sounder signals. Characterization of subsurface features.Characterization of subsurface features. Basis for the development of automatic techniques for the detection of subsurface features.Basis for the development of automatic techniques for the detection of subsurface features. Automatic information extraction from radargrams. Automatic information extraction from radargrams. First return.First return. Basal returns.Basal returns. Subsurface layering.Subsurface layering. Discrimination of surface clutter.Discrimination of surface clutter. Aim of the Work 6A. Ferro, L. Bruzzone

7. University of Trento, Italy Development of a processing framework for the automatic analysis of radar sounder data. Development of a processing framework for the automatic analysis of radar sounder data. Statistical analysis of radar sounder signals. Statistical analysis of radar sounder signals. Characterization of subsurface features.Characterization of subsurface features. Basis for the development of automatic techniques for the detection of subsurface features.Basis for the development of automatic techniques for the detection of subsurface features. Automatic information extraction from radargrams. Automatic information extraction from radargrams. First return.First return. Basal returns.Basal returns. Subsurface layering.Subsurface layering. Discrimination of surface clutter.Discrimination of surface clutter. Aim of the Work 7A. Ferro, L. Bruzzone

8. University of Trento, Italy SHARAD radargrams SHARAD radargrams Number of radargrams: 7Number of radargrams: 7 Area of interest: North Polar Layered Deposits (NPLD) of MarsArea of interest: North Polar Layered Deposits (NPLD) of Mars Resolution: 300 × 3000 × 15 m (along- track × across-track × range)Resolution: 300 × 3000 × 15 m (along- track × across-track × range) Dataset Description 8A. Ferro, L. Bruzzone -2500 m -5500 m SHARAD radargram 1319502

9. University of Trento, Italy Definition of targets: Definition of targets: NT: no targetNT: no target SL: strong layersSL: strong layers WL: weak layersWL: weak layers LR: low returnsLR: low returns BR: basal returnsBR: basal returns Proposed Approach: Statistical Analysis 9A. Ferro, L. Bruzzone Goal: Goal: Understand the statistical properties of the amplitude distribution underlying the scattering from different target classes. SHARAD radargram 1319502

10. University of Trento, Italy Tested statistical distributions (amplitude domain): Tested statistical distributions (amplitude domain): Rayleigh: simplest model, scattering from a large set of scatterers with the same size.Rayleigh: simplest model, scattering from a large set of scatterers with the same size. Nakagami: amplitude version of the Gamma distribution, has the Rayleigh has a particular case.Nakagami: amplitude version of the Gamma distribution, has the Rayleigh has a particular case. K: models the scattering from scatterers not homogeneously distributed in space, which number is a negative binomial random variable.K: models the scattering from scatterers not homogeneously distributed in space, which number is a negative binomial random variable. Distribution fitting performed via a Maximum Likelihood approach. Distribution fitting performed via a Maximum Likelihood approach. Goodness of fit tested by calculating the RMSE and the Kullback-Leibler distance (KL) between the target histogram and the fitted distribution. Goodness of fit tested by calculating the RMSE and the Kullback-Leibler distance (KL) between the target histogram and the fitted distribution. Proposed Approach: Statistical Analysis 10A. Ferro, L. Bruzzone Amplitude Mean power Shape parameter

11. University of Trento, Italy Proposed Approach: Statistical Analysis, Fitting 11A. Ferro, L. Bruzzone No target Weak layers Strong layers Low returns Basal returns Summary SHARAD radargram 1319502

12. University of Trento, Italy Best fitting distribution: K distribution Best fitting distribution: K distribution The parameters of the distribution describe statistically the characteristics of the target.The parameters of the distribution describe statistically the characteristics of the target. Noise can be modeled with a simple Rayleigh distribution. Noise can be modeled with a simple Rayleigh distribution. Results: Statistical Analysis 12A. Ferro, L. Bruzzone Radargram number Distribution No targetStrong layersWeak layersLow returnsBasal returns RMSEKLRMSEKLRMSEKLRMSEKLRMSEKL 0371502 Rayleigh0.00310.00670.00740.03810.01330.05160.01250.01080.01060.0243 Nakagami0.00310.00670.00320.01080.00750.01860.00850.00430.00790.0146 K0.00410.00680.00280.00600.00180.00210.00460.00280.00240.0033 0385902 Rayleigh0.00320.00290.01180.10350.01470.04750.01610.02930.01080.0313 Nakagami0.00310.00300.00680.04180.01030.02490.01210.01530.00920.0214 K0.00470.00310.00260.00670.00460.00560.00590.00420.00450.0058 0681402 Rayleigh0.00340.00450.00850.07070.02220.12580.01770.02470.01930.0675 Nakagami0.00340.00450.00540.02850.01410.05030.01390.01360.01490.0362 K0.00480.00460.00140.00310.00440.0054 0.00330.00600.0064 0794703 Rayleigh0.00410.00620.00270.00890.01880.07320.01220.01310.01550.0462 Nakagami0.00400.00600.00210.00520.01200.02930.00900.00680.01260.0283 K0.00520.00620.00140.00330.00390.00280.00310.00360.00520.0048 1292401 Rayleigh0.00460.00410.00520.02880.02130.10160.01520.01080.01570.0343 Nakagami0.00450.0043 0.02250.01400.04560.01160.00600.01240.0190 K0.00620.00420.00340.01100.00510.00740.00870.00250.00530.0058 1312901 Rayleigh0.00580.00480.00390.06230.02530.10930.01740.02720.01780.0357 Nakagami0.00580.00470.00430.05000.01640.04520.01490.01570.01250.0189 K0.00680.00480.00350.02520.00570.00610.00720.00650.00380.0026 1319502 Rayleigh0.00530.00910.00290.01350.01570.05400.02100.02020.01780.0585 Nakagami0.00530.00890.00220.01050.00790.01510.01660.01090.01400.0346 K0.00650.00910.00250.00820.00270.00290.00730.00350.00560.0070

13. University of Trento, Italy Proposed Approach: Automatic Detection of BR 13A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map Thresholding KL 1 BR seed selection Region growingfor m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growingRegion selection BR map generation KL m BR map

14. University of Trento, Italy Proposed Approach: Automatic Detection of BR 14A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map SHARAD radargram 1319502 Frame-based detection of the first return. Frame-based detection of the first return. Map of the KL HN : Map of the KL HN : Calculated for the subsurface area using a sliding window approach.Calculated for the subsurface area using a sliding window approach. It represents a meta-level between the amplitude data and the final product.It represents a meta-level between the amplitude data and the final product. Estimated noise distribution Local histogram

15. University of Trento, Italy Proposed Approach: Automatic Detection of BR 15A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map SHARAD radargram 1319502 Frame-based detection of the first return. Frame-based detection of the first return. Map of the KL HN : Map of the KL HN : Calculated for the subsurface area using a sliding window approach.Calculated for the subsurface area using a sliding window approach. It represents a meta-level between the amplitude data and the final product.It represents a meta-level between the amplitude data and the final product. Estimated noise distribution Local histogram

16. University of Trento, Italy Proposed Approach: Automatic Detection of BR 16A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map Frame-based detection of the first return. Frame-based detection of the first return. Map of the KL HN : Map of the KL HN : Calculated for the subsurface area using a sliding window approach.Calculated for the subsurface area using a sliding window approach. It represents a meta-level between the amplitude data and the final product.It represents a meta-level between the amplitude data and the final product. SHARAD radargram 1319502 KL HN map Estimated noise distribution Local histogram

17. University of Trento, Italy KL HN map Proposed Approach: Automatic Detection of BR 17A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map Selection of the regions with the highest probability to be related to the basal scattering area. Selection of the regions with the highest probability to be related to the basal scattering area. The initial BR map is created using a region growing approach based on level sets which starts from the seeds and moves on the KL HN map. The initial BR map is created using a region growing approach based on level sets which starts from the seeds and moves on the KL HN map. Level set function PropagationCurvature Initial BR map

18. University of Trento, Italy Proposed Approach: Automatic Detection of BR 18A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. Initial BR map

19. University of Trento, Italy Proposed Approach: Automatic Detection of BR 19A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. Step 2

20. University of Trento, Italy Proposed Approach: Automatic Detection of BR 20A. Ferro, L. Bruzzone First return detection Input radargram Calculation of KL HN KL HN map Initial BR map ThresholdingKL 1 BR seed selection Region growing for m=2 to M Estimation of BR statistics Thresholding BR seed selection Region growing Region selection BR map generation KL m BR map The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The initial BR map is used to estimate the statistical distribution of the amplitude of the BR samples. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The procedure is repeated iteratively using lower threshold ranges for the KL HN map. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted. Step 3

21. University of Trento, Italy Results: Automatic Detection of BR 21A. Ferro, L. Bruzzone SHARAD radargram 1319502 SHARAD radargram 0371502 SHARAD radargram 1292401 SHARAD radargram 1312901

22. University of Trento, Italy 22A. Ferro, L. Bruzzone The performance of the technique has been measured quantitatively. The performance of the technique has been measured quantitatively. Selection of 3000 reference samples randomly taken in areas of the radargram where BR returns are (or are not) visible.Selection of 3000 reference samples randomly taken in areas of the radargram where BR returns are (or are not) visible. Counted the number of samples correctly detected as BR (or not BR) returns.Counted the number of samples correctly detected as BR (or not BR) returns. Radargram number Feature samples Missed alarms % missed alarms Non-feature samples False alarms % false alarms Total error % total error 03715022503012.002,750371.35672.23 03859022815118.152,719301.10812.70 06814023406117.942,660592.221204.00 0794703282196.742,718712.61903.00 129240112497.262,876903.13993.30 131290124052.082,760933.37983.27 1319502271259.232,729802.931053.50 Average 255.428.610.492,744.665.72.3994.33.14 Results: Automatic Detection of BR

23. University of Trento, Italy Results: Layer Density Estimation 23A. Ferro, L. Bruzzone SHARAD radargram 052052 Automatic detection of linear interfaces Interface density map

24. University of Trento, Italy Conclusions 24A. Ferro, L. Bruzzone Developing a processing framework for the analysis of radar sounder data. Developing a processing framework for the analysis of radar sounder data. Statistical analysis of radar sounder signals. Statistical analysis of radar sounder signals. It can support the analysis of the radargrams.It can support the analysis of the radargrams. Different statistics / different targets.Different statistics / different targets. Generation of statistical maps useful to drive detection algorithms.Generation of statistical maps useful to drive detection algorithms. Novel technique for the automatic detection of the basal returns from radar sounder data using statistical techniques. Novel technique for the automatic detection of the basal returns from radar sounder data using statistical techniques. Effectively tested on SHARAD radargrams.Effectively tested on SHARAD radargrams. 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.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.

25. University of Trento, Italy Future Work 25A. Ferro, L. Bruzzone Improvements of the proposed technique: Improvements of the proposed technique: Estimation of local statistics using context-sensitive techniques for the adaptive determination of the local parcel size.Estimation of local statistics using context-sensitive techniques for the adaptive determination of the local parcel size. Develop a procedure for the automatic and adaptive definition of the parameters of the proposed technique.Develop a procedure for the automatic and adaptive definition of the parameters of the proposed technique. Adapt the algorithm to airborne acquisitions on Earth’s Poles.Adapt the algorithm to airborne acquisitions on Earth’s Poles. Other possible developments: Other possible developments: Integration of the automatic detection of linear interfaces and basal returns to higher level products.Integration of the automatic detection of linear interfaces and basal returns to higher level products. Automatic detection and filtering of surface clutter returns from the radargrams.Automatic detection and filtering of surface clutter returns from the radargrams.

26. University of Trento, Italy 26A. Ferro, L. Bruzzone Contacts: E-mail: adamo.ferro@disi.unitn.itE-mail: adamo.ferro@disi.unitn.itadamo.ferro@disi.unitn.it Website: http://rslab.disi.unitn.itWebsite: http://rslab.disi.unitn.ithttp://rslab.disi.unitn.it

27. University of Trento, Italy 27A. Ferro, L. Bruzzone BACKUP SLIDES

28. University of Trento, Italy Automatic Detection of Surface Clutter, Example 28A. Ferro, L. Bruzzone SHARAD radargram 1386001 Coregistered surface clutter simulation Detected surface clutter map

29. University of Trento, Italy Automatic Detection of the NPLD BR, Results 29A. Ferro, L. Bruzzone Coverage of selected 45 tracks 20 0 Depth of detected BR from detected surface return [µs] -2300 -4000 Mars North Pole topography [m] 86º 84º 88º 82º 0º 180º 90º270º Example of application to a large number of tracks

30. University of Trento, Italy Results: Automatic Detection of BR 30A. Ferro, L. Bruzzone SHARAD radargram 1319502 SHARAD radargram 0371502 SHARAD radargram 1292401 SHARAD radargram 1312901

31. University of Trento, Italy Model parameters 31A. Ferro, L. Bruzzone