Remote Sensing Laboratory
Dept. of Information Engineering and Computer Science
University of Trento
Via Sommarive, 14, I-38123 Povo, Trento, Italy
A Novel Approach to the Automatic
Detection of Subsurface Features in
Planetary Radar Sounder Signals
Adamo Ferro
Lorenzo Bruzzone
E-mail: adamo.ferro@disi.unitn.it
Web page: http://rslab.disi.unitn.it
Outline
1
Introduction
2
Aim of the Work
3
Statistical Analysis of Radar Sounder Signals
4
Automatic Detection of Basal Returns
5
Conclusions and Future Work
University of Trento, Italy
A. Ferro, L. Bruzzone
2
Introduction
 Planetary radar sounders can probe the
subsurface of the target body from orbit.
 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]
Nadir
Platform
height
Range (depth)
 Main instruments:
• Moon: ALSE and LRS
• Mars: MARSIS and SHARAD
v
 Radar sounder data have been analyzed
mostly by means of manual investigations.
[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
A. Ferro, L. Bruzzone
Example of radargram (SHARAD)
3
State of the Art
 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
A. Ferro, L. Bruzzone
4
Proposed Processing Framework
Ground
processing
Level 2 products
Raw data
Level 3 products
Labels
Map of interesting
areas
Icy layers
position
3D tomography of
icy layers
Basal returns
position
Ice thickness map
Level 1
products
Preprocessing
Information
extraction
...
Other inputs
...
...
(e.g., ancillary data,
clutter simulations)
University of Trento, Italy
A. Ferro, L. Bruzzone
5
Aim of the Work
 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.
University of Trento, Italy
A. Ferro, L. Bruzzone
6
Aim of the Work
 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.
University of Trento, Italy
A. Ferro, L. Bruzzone
7
Dataset Description
 SHARAD radargrams
• Number of radargrams: 7
• Area of interest: North Polar Layered
Deposits (NPLD) of Mars
• Resolution: 300 × 3000 × 15 m (alongtrack × across-track × range)
-2500 m
-5500 m
SHARAD radargram 1319502
University of Trento, Italy
A. Ferro, L. Bruzzone
8
Proposed Approach: Statistical Analysis
SHARAD radargram 1319502
 Goal:
Understand the statistical
properties of the amplitude
distribution underlying the
scattering from different target
classes.
University of Trento, Italy
 Definition of targets:
• NT: no target
• SL: strong layers
• WL: weak layers
• LR: low returns
• BR: basal returns
A. Ferro, L. Bruzzone
9
Proposed Approach: Statistical Analysis
 Tested statistical distributions (amplitude domain):
• Rayleigh: simplest model, scattering from a large set of scatterers with
the same size.
Amplitude
•
 x2 
pR ( x) 
exp  

z
 z 
2x
Nakagami: amplitude version of the Gamma distribution, has the
Rayleigh has a particular case.
v
p N ( x )  2  N
 z
•
Mean
power




vN
2 v N 1
 vN x 2 
x
exp  

 (v N )
z 

Shape
parameter
K: models the scattering from scatterers not homogeneously distributed
in space, which number is a negative binomial random variable.
 vK 


pK ( x) 

 ( v K )   z 
4
 v K 1 
2
x
vK

K v K 1  2 x

vK 

z 
Shape
parameter
 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.
University of Trento, Italy
A. Ferro, L. Bruzzone
10
Proposed Approach: Statistical Analysis, Fitting
SHARAD radargram 1319502
No target
Strong layers
Weak layers
Low returns
Basal returns
Summary
University of Trento, Italy
A. Ferro, L. Bruzzone
11
Results: Statistical Analysis
Radargram
number
0371502
0385902
0681402
0794703
1292401
1312901
1319502
Distribution
No target
Strong layers
Weak layers
Low returns
Basal returns
RMSE
KL
RMSE
KL
RMSE
KL
RMSE
KL
RMSE
KL
Rayleigh
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
0.0146
K
0.0041
0.0068
0.0028
0.0060
0.0018
0.0021
0.0046
0.0028
0.0024
0.0033
Rayleigh
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
0.0214
K
0.0047
0.0031
0.0026
0.0067
0.0046
0.0056
0.0059
0.0042
0.0045
0.0058
Rayleigh
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
0.0362
K
0.0048
0.0046
0.0014
0.0031
0.0044
0.0054
0.0054
0.0033
0.0060
0.0064
Rayleigh
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
0.0283
K
0.0052
0.0062
0.0014
0.0033
0.0039
0.0028
0.0031
0.0036
0.0052
0.0048
Rayleigh
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
0.0190
K
0.0062
0.0042
0.0034
0.0110
0.0051
0.0074
0.0087
0.0025
0.0053
0.0058
Rayleigh
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
0.0189
K
0.0068
0.0048
0.0035
0.0252
0.0057
0.0061
0.0072
0.0065
0.0038
0.0026
Rayleigh
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
0.0346
K
0.0065
0.0091
0.0025
0.0082
0.0027
0.0029
0.0073
0.0035
0.0056
0.0070
 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.
University of Trento, Italy
A. Ferro, L. Bruzzone
12
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation of
KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of BR
statistics
for m=2 to M
Initial BR map
Region growing
BR seed
selection
Region growing
Region selection
BR map
generation
BR map
University of Trento, Italy
A. Ferro, L. Bruzzone
13
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing

Frame-based detection of the first return.

Map of the KLHN:
KL
HN
Local histogram

 H ( x ) log
i
xi
BR seed
selection
Region
growing
Region
selection
•
BR map
generation
BR
map
•
H ( xi )
N ( xi )
Estimated noise
distribution
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
University of Trento, Italy
A. Ferro, L. Bruzzone
14
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing

Frame-based detection of the first return.

Map of the KLHN:
KL
HN
Local histogram

 H ( x ) log
i
xi
BR seed
selection
Region
growing
Region
selection
•
BR map
generation
BR
map
•
H ( xi )
N ( xi )
Estimated noise
distribution
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
University of Trento, Italy
A. Ferro, L. Bruzzone
15
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing

Frame-based detection of the first return.

Map of the KLHN:
KL
HN
Local histogram

 H ( x ) log
i
xi
BR seed
selection
Region
growing
Region
selection
•
BR map
generation
BR
map
•
H ( xi )
N ( xi )
Estimated noise
distribution
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
University of Trento, Italy
A. Ferro, L. Bruzzone
16
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
KLm
Thresholding
Estimation of
BR statistics
BR seed
selection
Region
growing
Region
selection
Thresholding
for m=2 to M
BR seed
selection
KL1
Initial BR
map
Region
growing
BR map
generation
d
BR
map
dt

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.
    P ( i , j )   C   

 KL HN ( i , j )  t L
P (i , j )  
 t U  KL HN ( i , j )
if KL
HN
(i, j ) 
tU  t L
2
 tL
otherwise
Level set Propagation Curvature
function
KLHN map
Initial BR map
University of Trento, Italy
A. Ferro, L. Bruzzone
17
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing
BR seed
selection
Region
growing
Region
selection
BR map
generation
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
A. Ferro, L. Bruzzone
18
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing
BR seed
selection
Region
growing
Region
selection
BR map
generation
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
A. Ferro, L. Bruzzone
19
Proposed Approach: Automatic Detection of BR
Input
radargram
First return
detection
Calculation
of KLHN
KLHN map
Thresholding
KL1
BR seed
selection
KLm
Thresholding
Estimation of
BR statistics
for m=2 to M
Initial BR
map
Region
growing
BR seed
selection
Region
growing
Region
selection
BR map
generation
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
A. Ferro, L. Bruzzone
20
Results: Automatic Detection of BR
SHARAD radargram 1319502
SHARAD radargram 0371502
SHARAD radargram 1292401
University of Trento, Italy
A. Ferro, L. Bruzzone
SHARAD radargram 1312901
21
Results: Automatic Detection of BR
 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
Feature
samples
Missed
alarms
% missed
alarms
Non-feature
samples
False
alarms
% false
alarms
Total
error
% total
error
0371502
250
30
12.00
2,750
37
1.35
67
2.23
0385902
281
51
18.15
2,719
30
1.10
81
2.70
0681402
340
61
17.94
2,660
59
2.22
120
4.00
0794703
282
19
6.74
2,718
71
2.61
90
3.00
1292401
124
9
7.26
2,876
90
3.13
99
3.30
1312901
240
5
2.08
2,760
93
3.37
98
3.27
1319502
271
25
9.23
2,729
80
2.93
105
3.50
Average
255.4
28.6
10.49
2,744.6
65.7
2.39
94.3
3.14
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A. Ferro, L. Bruzzone
22
Results: Layer Density Estimation
SHARAD radargram 052052
Automatic detection of linear interfaces
Interface density map
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A. Ferro, L. Bruzzone
23
Conclusions
 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.
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A. Ferro, L. Bruzzone
24
Future Work
 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
A. Ferro, L. Bruzzone
25
Contacts:
•
E-mail: adamo.ferro@disi.unitn.it
•
Website: http://rslab.disi.unitn.it
University of Trento, Italy
A. Ferro, L. Bruzzone
26
BACKUP
SLIDES
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A. Ferro, L. Bruzzone
27
Automatic Detection of Surface Clutter, Example
SHARAD radargram 1386001
Coregistered surface clutter simulation
Detected surface clutter map
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A. Ferro, L. Bruzzone
28
Automatic Detection of the NPLD BR, Results
Example of application to a large number of tracks
Mars North Pole topography [m]
-2300
Coverage of selected 45 tracks
Depth of detected BR from
detected surface return [µs]
20
180º
270º
90º
88º
86º
84º
82º
0º
-4000
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0
A. Ferro, L. Bruzzone
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Results: Automatic Detection of BR
SHARAD radargram 1319502
SHARAD radargram 0371502
SHARAD radargram 1292401
University of Trento, Italy
A. Ferro, L. Bruzzone
SHARAD radargram 1312901
30
Model parameters
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A. Ferro, L. Bruzzone
31