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: [email protected]Web page: http:// rslab.disi.unitn.it
A Novel Approach to the Automatic Detection of Subsurface Features in Planetary Radar Sounder Signals. Adamo Ferro Lorenzo Bruzzone. E-mail: [email protected] Web page: http:// rslab.disi.unitn.it. Outline. Introduction. 1. Aim of the Work. 2. - PowerPoint PPT Presentation
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Remote Sensing LaboratoryDept. of Information Engineering and Computer Science
University of TrentoVia Sommarive, 14, I-38123 Povo, Trento, Italy
Adamo FerroLorenzo Bruzzone
A Novel Approach to the Automatic Detection of Subsurface Features in
Planetary radar sounders can probe the subsurface of the target body from orbit.
Main instruments:• Moon: ALSE and LRS• Mars: MARSIS and SHARAD
Their effectiveness lead to the proposal of new orbiting radar sounders, also for Earth science:• IPR and SSR for the Jovian Moons[1]
• GLACIES proposal for the Earth[2]
Radar sounder data have been analyzed mostly by means of manual investigations.
v
Ran
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epth
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Across track
Platform height
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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.
University of Trento, Italy
State of the Art
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Past works related to the automatic analysis of radar sounder data regard the analysis of ground-based or airborne GPR signals.• Different frequency ranges.• Better spatial resolution.• Detection of buried objects (e.g., mines, pipes) which show specific
signatures (e.g., hyperbolas).• Investigation of local targets vs. regional and global mapping.
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.
University of Trento, Italy
Proposed Processing Framework
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Raw data
Ground processing
Level 1products
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
...
University of Trento, Italy
Development of a processing framework for the automatic analysis of radar sounder data.
Statistical analysis of radar sounder signals.• Characterization of subsurface features.• Basis for the development of automatic techniques for the
detection of subsurface features.
Automatic information extraction from radargrams.• First return.• Basal returns.• Subsurface layering.• Discrimination of surface clutter.
Aim of the Work
6A. Ferro, L. Bruzzone
University of Trento, Italy
Development of a processing framework for the automatic analysis of radar sounder data.
Statistical analysis of radar sounder signals.• Characterization of subsurface features.• Basis for the development of automatic techniques for the
detection of subsurface features.
Automatic information extraction from radargrams.• First return.• Basal returns.• Subsurface layering.• Discrimination of surface clutter.
Aim of the Work
7A. Ferro, L. Bruzzone
University of Trento, Italy
SHARAD radargrams• Number of radargrams: 7• Area of interest: North Polar Layered
Deposits (NPLD) of Mars• Resolution: 300 × 3000 × 15 m (along-
track × across-track × range)
Dataset Description
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-2500 m
-5500 m
SHARAD radargram 1319502
University of Trento, Italy
Definition of targets:• NT: no target• SL: strong layers• WL: weak layers• LR: low returns• BR: basal returns
Proposed Approach: Statistical Analysis
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Goal: Understand the statistical properties of the amplitude distribution underlying the scattering from different target classes.
SHARAD radargram 1319502
University of Trento, Italy
Tested statistical distributions (amplitude domain):• 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.
• 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. 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
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Amplitude Mean power
Shape parameter
Shape parameter
University of Trento, Italy
Proposed Approach: Statistical Analysis, Fitting
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No target Weak layersStrong layers
Low returns Basal returns Summary
SHARAD radargram 1319502
University of Trento, Italy
Best fitting distribution: K distribution• The parameters of the distribution describe statistically the
characteristics of the target. Noise can be modeled with a simple Rayleigh distribution.
Results: Statistical Analysis
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Radargramnumber Distribution
No target Strong layers Weak layers Low returns Basal returns
Developing a processing framework for the analysis of radar sounder data.
Statistical analysis of radar sounder signals.• It can support the analysis of the radargrams.• Different statistics / different targets.• 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.• 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.
University of Trento, Italy
Future Work
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Improvements of the proposed technique:• 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.• Adapt the algorithm to airborne acquisitions on Earth’s Poles.
Other possible developments:• 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.
University of Trento, Italy 26A. Ferro, L. Bruzzone