Adamo Ferro Lorenzo Bruzzone
description
Transcript of Adamo Ferro Lorenzo Bruzzone
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 Sounder Signals
E-mail: [email protected] page: http://rslab.disi.unitn.it
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 Returns4
Conclusions and Future Work5
University of Trento, Italy
Introduction
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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
ge (d
epth
)
Across track
Platform height
Nad
ir
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.
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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
8A. Ferro, L. Bruzzone
-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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xvv
xvxpN
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exp)(
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Kv
vv
z
K
KK
vxKxvv
xpK
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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
RMSE KL RMSE KL RMSE KL RMSE KL RMSE KL
0371502Rayleigh 0.0031 0.0067 0.0074 0.0381 0.0133 0.0516 0.0125 0.0108 0.0106 0.0243
Nakagami 0.0031 0.0067 0.0032 0.0108 0.0075 0.0186 0.0085 0.0043 0.0079 0.0146K 0.0041 0.0068 0.0028 0.0060 0.0018 0.0021 0.0046 0.0028 0.0024 0.0033
0385902Rayleigh 0.0032 0.0029 0.0118 0.1035 0.0147 0.0475 0.0161 0.0293 0.0108 0.0313
Nakagami 0.0031 0.0030 0.0068 0.0418 0.0103 0.0249 0.0121 0.0153 0.0092 0.0214K 0.0047 0.0031 0.0026 0.0067 0.0046 0.0056 0.0059 0.0042 0.0045 0.0058
0681402Rayleigh 0.0034 0.0045 0.0085 0.0707 0.0222 0.1258 0.0177 0.0247 0.0193 0.0675
Nakagami 0.0034 0.0045 0.0054 0.0285 0.0141 0.0503 0.0139 0.0136 0.0149 0.0362K 0.0048 0.0046 0.0014 0.0031 0.0044 0.0054 0.0054 0.0033 0.0060 0.0064
0794703Rayleigh 0.0041 0.0062 0.0027 0.0089 0.0188 0.0732 0.0122 0.0131 0.0155 0.0462
Nakagami 0.0040 0.0060 0.0021 0.0052 0.0120 0.0293 0.0090 0.0068 0.0126 0.0283K 0.0052 0.0062 0.0014 0.0033 0.0039 0.0028 0.0031 0.0036 0.0052 0.0048
1292401Rayleigh 0.0046 0.0041 0.0052 0.0288 0.0213 0.1016 0.0152 0.0108 0.0157 0.0343
Nakagami 0.0045 0.0043 0.0043 0.0225 0.0140 0.0456 0.0116 0.0060 0.0124 0.0190K 0.0062 0.0042 0.0034 0.0110 0.0051 0.0074 0.0087 0.0025 0.0053 0.0058
1312901Rayleigh 0.0058 0.0048 0.0039 0.0623 0.0253 0.1093 0.0174 0.0272 0.0178 0.0357
Nakagami 0.0058 0.0047 0.0043 0.0500 0.0164 0.0452 0.0149 0.0157 0.0125 0.0189K 0.0068 0.0048 0.0035 0.0252 0.0057 0.0061 0.0072 0.0065 0.0038 0.0026
1319502Rayleigh 0.0053 0.0091 0.0029 0.0135 0.0157 0.0540 0.0210 0.0202 0.0178 0.0585
Nakagami 0.0053 0.0089 0.0022 0.0105 0.0079 0.0151 0.0166 0.0109 0.0140 0.0346K 0.0065 0.0091 0.0025 0.0082 0.0027 0.0029 0.0073 0.0035 0.0056 0.0070
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
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First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of BR statisticsThresholding
BR seed selection Region growing Region selection BR map
generation
KLm
BR map
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
14A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
SHARAD radargram 1319502
Frame-based detection of the first return.
Map of the KLHN:
• Calculated for the subsurface area using a sliding window approach.
• It represents a meta-level between the amplitude data and the final product.
ix i
iiHN xN
xHxH)()(log)(KL Estimated noise
distribution
Local histogram
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
15A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
SHARAD radargram 1319502
Frame-based detection of the first return.
Map of the KLHN:
• Calculated for the subsurface area using a sliding window approach.
• It represents a meta-level between the amplitude data and the final product.
ix i
iiHN xN
xHxH)()(log)(KL Estimated noise
distribution
Local histogram
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
16A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
Frame-based detection of the first return.
Map of the KLHN:
• Calculated for the subsurface area using a sliding window approach.
• It represents a meta-level between the amplitude data and the final product.
SHARAD radargram 1319502
KLHN map
ix i
iiHN xN
xHxH)()(log)(KL Estimated noise
distribution
Local histogram
University of Trento, Italy
KLHN map
Proposed Approach: Automatic Detection of BR
17A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
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 KLHN map.
CjiPdtd ),(
otherwise),(KL
2),(KL if),(KL),(
jit
ttt
jitjijiPHNU
LLU
HNLHN
Level set function
Propagation Curvature
Initial BR map
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
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First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
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 KLHN map.
The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.
Initial BR map
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
19A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
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 KLHN map.
The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.
Step 2
University of Trento, Italy
Proposed Approach: Automatic Detection of BR
20A. Ferro, L. Bruzzone
First return detection
Inputradargram
Calculation of KLHN
KLHN map
Initial BR map
Thresholding KL1BR seed selection
Region growingfor m=2 to MEstimation of
BR statisticsThresholding
BR seed selection
Region growing
Region selection
BR map generation
KLm
BR map
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 KLHN map.
The new regions created during the iterations which are not statistically similar to the estimated BR distribution are deleted.
Step 3
University of Trento, Italy
Results: Automatic Detection of BR
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SHARAD radargram 1319502
SHARAD radargram 0371502
SHARAD radargram 1292401 SHARAD radargram 1312901
University of Trento, Italy 22A. Ferro, L. Bruzzone
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.• Counted the number of samples correctly detected as BR (or not BR)
returns.
Radargram number
Featuresamples
Missedalarms
% missedalarms
Non-featuresamples
Falsealarms
% falsealarms
Totalerror
% totalerror
0371502 250 30 12.00 2,750 37 1.35 67 2.230385902 281 51 18.15 2,719 30 1.10 81 2.700681402 340 61 17.94 2,660 59 2.22 120 4.000794703 282 19 6.74 2,718 71 2.61 90 3.001292401 124 9 7.26 2,876 90 3.13 99 3.301312901 240 5 2.08 2,760 93 3.37 98 3.271319502 271 25 9.23 2,729 80 2.93 105 3.50Average 255.4 28.6 10.49 2,744.6 65.7 2.39 94.3 3.14
Results: Automatic Detection of BR
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Results: Layer Density Estimation
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SHARAD radargram 052052
Automatic detection of linear interfaces
Interface density map
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Conclusions
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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.
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Contacts:• E-mail: [email protected]• Website: http://rslab.disi.unitn.it
Thank you for your attention!
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BACKUPSLIDES
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Automatic Detection of Surface Clutter, Example
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SHARAD radargram 1386001
Coregistered surface clutter simulation
Detected surface clutter map
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Automatic Detection of the NPLD BR, Results
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Coverage of selected 45 tracks20
0
Depth of detected BR fromdetected surface return [µs]-2300
-4000
Mars North Pole topography [m]
86º
84º
88º
82º0º
180º
90º270º
Example of application to a large number of tracks
University of Trento, Italy
Results: Automatic Detection of BR
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SHARAD radargram 1319502
SHARAD radargram 0371502
SHARAD radargram 1292401 SHARAD radargram 1312901
University of Trento, Italy
Model parameters
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