Region of Interest (RoI)

Detection in Ground

Penetrating Radar (GPR) Data2D ENTROPY ANALYSIS

Presenter: Yu Zhang

AutobiographyRESEARCH INTERESTS

2

Autobiography

University of Vermont

Doctor of Philosophy (Ph.D.) in Electrical Engineering

2012 – Present

Advisor: Dr. Tian Xia

Huazhong University of Science and Technology

Bachelor of Science (B.S.) in Electrical Engineering

2008 – 2012

3

Research Interests

Compressive OFDM GPR

Tx Rx

Rebar

Concrete

30% Compression

5% Compression

Full OFDM Spectrum

4

Research Interests

Synthetic Aperture Radar (SAR)

based GPR imaging

Cylinder

Triangle

Rectangle

Test Scenario Regular GPR B-Scan Image GPR-SAR B-Scan Image

5

Research Interests

Low-Rank and Sparse Representation in

GPR and Through-the-Wall Radar Imaging

Test Scenario GPR B-Scan

Regular Clutter Removal

L + S Representation

6

Research Interests

GPR Signal Processing related

Problems

Image De-noising

Clutter Removal

Image Migration

Region of Interest (RoI) Detection

7

IntroductionWHY GPR, GPR OPERATING MECHANISM & OUR

GPR SYSTEM

8

Why GPR?

Non-destructive evaluation (NDE) of transportation

infrastructure.

GPR as a highly efficient NDE method:

Concrete bridge deck inspection;

Asphalt pavement monitoring;

Highway rebar detection;

Railroad ballast condition assessment,

Soil moisture estimation.

9

GPR Operating

Mechanism

Subsurface medias of different dielectric constants

Each position: A-Scan trace

Assemble all A-Scan traces: B-Scan image

10

Home made GPR System

Ultra-wide band (UWB) pulse generator

UWB antenna

High speed digitizer configuration

Wheel encoder

FPGA controller

PC & LabVIEW user interface

11

Prototype 12

Xia & Huston sitting on GPR 13

A ROBUST system package that can hold two adults’ weight!!!

Air-coupled Impulse GPR

System Specifications

Data acquisition unit 8 Gsps, 10-bit resolution

Sampling window width 40 ns

Pulse repetition frequency 0 to 30 kHz tunable

Horizontal resolution 1 cm at 60 miles/h survey speed

Signal bandwidth 600 MHz to 2 GHz tunable

Penetrating capability Up to 1 meter

14

Today’s TopicROI DETECTION IN GPR DATA USING 2-D ENTROPY

15

Why 2-D Entropy

Large volume (overall 20 miles) railroad GPR data set

collected during the field test at Metro St. Louis and

Massachusetts Bay Transit Authority.

Data collection in St. Louis Forest Park Station to Sunnen Station

High resolution GPR system brings us ~300 GB data.

How to process such large volume data???

16

Why 2-D Entropy

Large data volume

Sophisticated data processing is too computationally

complex and even infeasible

Properties of subsurface scatters or material are too

complex

Obtain of prior knowledge or training data is unrealistic

It is desirable to

Develop an unsupervised and automatic GPR data

processing method that can effectively and rapidly

identify suspicious features from big radargram

17

First Glance at 2-D

Entropy Analysis

Entropy is a measure of the uncertainty associated

with a random variable.

Entropy characterization is explored to identify singular

regions within a large GPR data set

High entropy value indicates high similarity

Low entropy value specifies high singularity

18

What do we have in a GPR

railroad B-Scan image

19

Noises Clutter

Cross-ties

or Sleepers

Useless

Background

Multiple Peaks from

one Scattering

Unsupervised GPR RoI

Detection based on Entropy

Raw Data

Stacking Every 50 Traces

Low Pass Filter

Hilbert Transform

A-Scan Decomposition

Region of Interest

2-D Entropy Analysis

Stacking Every 10 Trace

Clutter Removal

Subsurface Identification

Pre-processingPower Information

Characterization

Subsurface

Identification

B-Scan Image EnhancementRoI Detection

20

Pre-Processing

Step 1: Stack every 50 A-scan traces to calculate the

average to boost the signal-to-noise ratio (SNR).

o The selection of 50 traces for calculation considers the

balance between the obtainable image resolution and

noise reduction performance

Step 2: Apply Low Pass Filtering (LPF) with a 2 GHz

cutoff frequency.

o GPR transmission signal’s frequency: 600 MHz – 2 GHz

21

Power Information

Characterization

Hilbert Transform is implemented to extract the pulse

envelope.

Hilbert Transform of signal 𝑠 𝑡

𝑠 𝑡 = ℋ 𝑠 = ℎ 𝑡 ∗ 𝑠 𝑡 =1

𝜋𝑡∗ 𝑠(𝑡)

Analytic signal

𝑠𝑎 𝑡 = 𝑠 𝑡 + 𝑖 𝑠(𝑡)

Signal’s envelope

𝑠𝑎 𝑡 = 𝑠 𝑡2 + 𝑠(𝑡)2

22

Power Information

Characterization (con.)

Ricker wavelet source – 3 peaks

1st pulse - antennas’ direct coupling

2nd pulse - reflection signal from 1st scatter

3rd pulse – reflection signal from 2nd scatter

GPR A-Scan trace GPR A-Scan envelope

23

Subsurface Identification

A-Scan decomposition is performed to isolate

subsurface layer and narrow down the scope of data

Transmitter and receiver antennas’ direct coupling

pulse as the reference signal

By performing iterative cross correlations, an A-Scan

waveform is decomposed into the combinations of

multiple pulses of varying amplitude and phases

characterizing the reflection signals from different

scatters

24

Subsurface Identification

(con.)

Direct coupling signal 1st backscattering pulse 2nd backscattering pulse

25

B-Scan Image

Enhancement

Step 1: Remove the background signal using a 2-D

High Pass Filter (HPF)

o In horizontal direction, the frequency bandwidth of clutter

is much narrower than that of subsurface scattering

signals.

Step 2: Stack every 10 A-scan traces

o Further improve signal SNR as well as reduce data

volume and redundancy

26

Renyi’s Entropy

GPR backscattering signal 𝑌 𝑡 can be modeled as

𝑌 𝑡 = 𝐷 𝑡 + 𝑆(𝑡)

𝐷 𝑡 - reflection signal from objects of interest, 𝑆(𝑡) -

background signals.

Power normalization is first performed

𝑦𝑖 𝑡 =𝑌𝑖(𝑡)

2

𝑖=1𝑀 𝑌𝑖(𝑡)

2

𝑦𝑖 𝑡 - normalized signal, 𝑖 - trace index, 𝑀 – total

number of traces included, 𝑡 - time index of data points on

each trace.

Renyi’s entropy: 𝐸𝛼 𝑡 =1

1−𝛼loge 𝑖=1

𝑀 𝑦𝑖 𝑡𝛼

𝛼 – the entropy order. (𝛼 = 3 here)

27

2-D Entropy Based RoI

Detection

Step 1: 2-D Renyi entropy calculation

Step 2: Entropy curve smoothing using moving average

method:

𝐸𝑠𝑚𝑜𝑜𝑡ℎ 𝑛 =1

𝑚

𝑖=𝑛−𝑚+1

𝑛

𝐸(𝑖)

𝐸(𝑖) – entropy value of 𝑖th data in entropy sequence, 𝑚 is

selected as 5% of the total number of entropy data points

o Optimal smoothing performance as well as data resolution

Step 3: Adaptive entropy threshold determination using OTSU

method.

o Minimize inner-group variance & maximize inter-group variance

28

Lab Experiment

Railroad ballast test platform Side view for the subsurface structure

The ballast layer of 0.3 meters thickness is laid above the

soil

0.75 meters apart from the left end of the platform, an

area of 0.45 meters length and 0.2 meters depth is filled

with the fouled ballast, which is a mixture of sand, ballast,

and water

29

RoI Detection (1)

Raw B-Scan image Pre-processed B-Scan image

30

RoI Detection (2)

Signal Envelope Signal Decomposition

31

RoI Detection (3)

Ballast layer image Enhanced B-Scan

ballast layer image

32

RoI Detection (4)

Entropy along Travel Time (y-axis) Entropy along Scan Axis (x-axis)

33

RoI Detection (5)

RoI Detection Result based on 2-D Entropy analysis

34

Railroad Ballast Field Test

Home made GPR system mounted on vehicle during field tests

35

Test Sites

Metro St. Louis MetroLink blue line

(1:00~4:00 am)

Railroad ballast

36

RoI Detection (1)

Raw B-Scan image Pre-processed B-Scan image

37

RoI Detection (2)

Signal Envelope Cross-tie marked by

signal decomposition

38

RoI Detection (3)

Ballast layer image Enhanced B-Scan

ballast layer image

39

RoI Detection (4)

Entropy along Travel Time (y-axis) Entropy along Scan Axis (x-axis)

40

RoI Detection (5)

Suspicious fouled ballast regions marked by white rectangle

41

Conclusions

The unsupervised automatic ROI detection method

developed in this study can effectively identify regions

of interest in subsurface for further in-depth analysis.

The proposed unsupervised automatic GPR data

processing algorithm has been effectively applied to

laboratory and field test data.

The analysis results prove that the proposed algorithm

can correctly identify the region of interest and can

accurately measure the region’s location.

42

Future Work

Local entropy & multi-scale entropy analysis

43

Q & A?

Q&A?

44