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

mailto:[email protected]

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

3A. Ferro, L. Bruzzone

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.


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.


Proposed Processing Framework


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

...


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



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



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


-2500 m

-5500 m

SHARAD radargram 1319502


Definition of targets:• NT: no target• SL: strong layers• WL: weak layers• LR: low returns• BR: basal returns

Proposed Approach: Statistical Analysis


Goal: Understand the statistical properties of the amplitude distribution underlying the scattering from different target classes.



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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exp2)(

z

N

N

vv

z

NN

xvv

xvxpN

N

212

exp)(

2)(

z

Kv

vv

z

K

KK

vxKxvv

xpK

K

K

2

)(4)( 1

21

Amplitude Mean power

Shape parameter

Shape parameter


Proposed Approach: Statistical Analysis, Fitting


No target Weak layersStrong layers

Low returns Basal returns Summary



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


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


Proposed Approach: Automatic Detection of BR


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





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map


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.

ix i

iiHN xN

xHxH)()(log)(KL Estimated noise

distribution

Local histogram





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




BR seed selection

Region growing

Region selection

BR map generation

KLm

BR map



Map of the KLHN:



ix i

iiHN xN


distribution

Local histogram





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




BR seed selection

Region growing

Region selection

BR map generation

KLm

BR map


Map of the KLHN:




KLHN map

ix i

iiHN xN


distribution

Local histogram


KLHN map




Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




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





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




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





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




BR seed selection

Region growing

Region selection

BR map generation

KLm

BR map




Step 2





Inputradargram

Calculation of KLHN

KLHN map

Initial BR map




BR seed selection

Region growing

Region selection

BR map generation

KLm

BR map




Step 3


Results: Automatic Detection of BR




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: Layer Density Estimation



Automatic detection of linear interfaces

Interface density map


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.


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.


Contacts:• E-mail: [email protected]• Website: http://rslab.disi.unitn.it

Thank you for your attention!

mailto:[email protected]



BACKUPSLIDES


Automatic Detection of Surface Clutter, Example



Coregistered surface clutter simulation

Detected surface clutter map


Automatic Detection of the NPLD BR, Results


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






SHARAD radargram 1292401 SHARAD radargram 1312901


Model parameters


Documents

Adamo Ferro Lorenzo Bruzzone