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Data Processing for Classification
Dean Keiswetter, Ph.D., M.B.A.
Chief Scientist, Leidos Holdings Inc.
Data Processing – Objective
● Determine which anomalies, if any, result from buried
munitions
● The result of the analysis is a decision regarding the
nature of the source of the measured signatures
● The analysis should be:
Transparent
Quantitative
Documented
2
+
UX-Analyze =
Commercial mapping,
processing, &
visualization software for
earth sciences
0Ht
H
t
H 2
2
2
Induced EMI
ResponsePhysics
Principal Axis Polarizabilities
+
UX-Analyze =
Commercial mapping,
processing, &
visualization software for
earth sciences
0Ht
H
t
H 2
2
2
Induced EMI
ResponsePhysics
Principal Axis Polarizabilities
Analysis
Algorithms
UX-Analyze software (ESTCP MR-0910)
+
UX-Analyze =
Commercial mapping,
processing, &
visualization software for
earth sciences
0Ht
H
t
H 2
2
2
Induced EMI
ResponsePhysics
Principal Axis Polarizabilities
+
UX-Analyze =
Commercial mapping,
processing, &
visualization software for
earth sciences
0Ht
H
t
H 2
2
2
Induced EMI
ResponsePhysics
Principal Axis Polarizabilities
Metal Mapper
TEMTADS 5x5
TEMTADS 2x2
Classification
Sensors
Commercial
mapping,
processing, &
visualization
software
Data analysis algorithms embedded into
Geosoft’s Oasis montaj…
Software Overview
● UX-Analyze is fully integrated into Oasis montaj as a
menu driven set of functions for geophysical target
characterization and classification.
● These functions permit users to effectively classify buried
sources as Targets-of-Interest, or not.
● Released to the US Government and commercial
contractors (free of charge to recipients)
4
Topics
● Review Polarizations – the basis of the classification
decision
● Processing Fundamentals
1. Construct a Library (Expected Munitions and Clutter)
2. QC Measured Data (Blind and Background)
3. Invert and Look for Expected Munitions
4. Look for Unexpected Munitions
5. Prioritize
● Results and Final Products
5
Polarizations – the Basis of the Decision
6
Plan view of TEM
Dipole
Response Model
Location & Orientation +
Time (ms)
0.1 1 10
Po
lari
za
bili
ty
10-3
10-2
10-1
100
101
P1
P2
P3
Polarizations
Transients
from Rx
cube
Time (ms)
0.1 1 10
Pola
rizabili
ty (
m3/A
)
0.001
0.01
0.1
1
10
Polarizabilities
7
Time (ms)
0.1 1 10
Pola
rizabili
ty (
m3/A
)
0.001
0.01
0.1
1
Principal axis polarizabilities
completely describe EM response
of target
intrinsic to the target
invariant to burial depth or
target orientation
Topics
● Polarizations – the basis of the classification decision
● Processing Flow Fundamentals
1. Construct a Library (Expected Munitions and Clutter)
2. QC Measured Data (Blind and Background)
3. Invert and Look for Expected Munitions
4. Look for Unexpected Munitions
5. Prioritize
● Results and final products
8
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
BTW - the processing flow applies to all
of the Advanced EMI Sensors
Advanced EMI sensors
are physically different,
but the extracted
polarizabilities are not...
9
100
100
sphere
100
100
105
100
100
60
100
100
small iso
100
100
37
100
100
sphere
100
100
105
100
100
60
100
100
small iso
100
100
37
Sphere 105 mm
60 mm Small ISO
time (ms) time (ms)
time (ms) time (ms)
MetalMapper
MPV
HandHeld BUD
TEMTADS 2x2
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
#1: Construct a Library
Document what we are Looking for…
Simply stated:
We want to specify which munitions are present at our
current site and store their respective polarizations in a library.
New items can be added to the list if encountered.
Why it is important:
Classification performance suffers if we look for munition
items that are not actually present…
10
Library Master
11
Database, maps
and scatter plot are
linked
Build a library for the munitions expected at your site. It
should include all anticipated munitions and unique
clutter items (if any).
Munitions Signature Examples
12
105mm 2.36inch
37mm
155mm
81mm
20mm
Munitions Variability
13
Variability among munitions
needs to be considered.
Site specific varieties,
whether anticipated or
discovered during the
program, must be added
to the library if unique…
Seeds can be Used to Verify Library
14
‘Manage Library Tool’:
• Checks adequacy of library…
• Identifies clusters…
Library
Overlay
Color coded
by number of
matches
0, 2, 5, 10, 20
Small ISO
Library
Current
Target Close
Matches
Small ISO Ellis TP1118
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
#2: QC Measured Data
Simply stated:
We need to examine the integrity of the measured data
files and the individual sensor readings
Why it is important:
Garbage in garbage out…
15
QC Sensor Data for Blind Sources
Sensor…
Spatial
registration…
GPS
Orientation
data…
16
GPS Data
Inertial Measurement Unit
Evaluate Sensor Data bounds…take Action on Outliers
EMI Sensor Data
QC Data Backgrounds
17
Sensor data acquired
over areas believed to be
free of metallic objects
are subtracted to remove
sensor drift, biases, and
ground response
Background Example
18
R6 R7
Tx
Ty
Tz
R4 R5 R1 R2 R3
3 backgrounds are different from
the rest in varying degrees
1150 (orange) is most variable
followed by 1111 (green) and
1265 (red)
1150
1111
1265
Z_6Y
Low amplitude signatures are preferentially
affected by questionable backgrounds
19
37mm Result of subtracting the three
different backgrounds shown
earlier on a seeded 37mm…
1265 as background
37mm; 0.86 metric
1111 as background
37mm; 0.35 metric
1150 as background
No Match
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
#3: Invert and Look for Expected Munitions
Simply stated:
In this step, we want to invert the measured sensor data
to obtain polarizabilities and compare them against our
library of anticipated munitions.
Why it is important:
Provides a quantitative comparison of intrinsic source
features
20
Primary ‘Turn the Crank’
21 Parameters
Process
Time (ms)
0.1 1 10
Po
lari
za
bili
ty10-3
10-2
10-1
100
101
P1
P2
P3
Location &
Orientation +
Inversion Process
Qualitative Visual-based Library Match…
Blue Unknown
Grey Library
from APG
60mm 81mm
105mm
Intr
insic
Pola
riza
tions
Intr
insic
Pola
riza
tions
0.1 1.0 10.0
10 -6
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
10 1
10 -6
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
10 1
0.1 1.0 10.0
0.1 1.0 10.0
10 -6
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
10 1
105mm
Time (ms)
Time (ms)
Time (ms)
22
23
Parameters
Options
Multiple sources view
All Three Curves
Only Two Curves
Only One Curve
0.93
0.94
0.99
Match Type (ID)
Quantitative Library Match
UX-Analyze: QC environment…
24
Interactive review with multiple linked views
scatterplot, database, images, and polarizations
A mouse click in the scatterplot or database changes all…
User can zoom to see scatterplot details…
Cluster Library Match Data maps
Size / Decay
Database
Fit Results
BTW -- It ain’t that easy to
unravel issues…
25
Lots and Lots of Individual Steps… • Analysts need training and practice
• Third party QC review is critical
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
#4: Look for Unexpected Munitions
26
Simply stated:
We need to look for repeat source signatures (multiple
sources that are similar) that are not explained by the
library
Why it is important:
We have to expect the unexpected. Historical records may
be incomplete. Site usage may vary by location…
Unanticipated UXO May Need to be Added
27
Library
Current
Target
Close
Matches
‘Manage Library Tool’:
• Checks adequacy of library…
• Identifies clusters…
Library
Overlay
Color coded
by number of
matches
0, 2, 5, 10, 20
Unanticipated TOI: Fort Sill, OK
?? – unique polarizations and best library match
28
29
3.5inch rocket
??
Unanticipated TOI: Fort Sill, OK
Fort Sill, OK – Ground Truth
40mm Frag Ball – no training data, unexpected
30
Construct a Library
QC Measured Data
Invert and Look for Expected
Munitions
Look for Unexpected Munitions
Prioritize
#5: Prioritization
31
Simply stated:
An anomaly is flagged for digging if the inverted source:
Matches a signature in the site-specific library of munitions
Is part of a previously unidentified cluster
Large and deeply buried
Why it is important:
A numerical process produces a transparent,
quantitative, and consistent classification decision
Prioritization Decision & Validation Digs
32
Library Match Metrics:
Can be simplified to a
single threshold -- one
that is chosen to account
for all site specific UXO.
Possible approach for recommending Validation Digs:
#1: Interrogate x% of anomalies below selected threshold
#2: Analyst recommended checks
Parameters
Bounds set by controlled
tests & rules to deal with
distance-to-flag scenarios
This Section:
Not Site Dependent
Topics
● Polarizations – the basis of the classification decision
● Processing Fundamentals
1. Construct a Library (Expected Munitions and Clutter)
2. QC Measured Data (Blind and Background)
3. Invert and Look for Expected Munitions
4. Look for Unexpected Munitions
5. Prioritize
● Results and Final Processing Products
33
Results and Final Processing
Products
34
Prioritized Dig List
The decision…
• Transparent
• Quantitative
• Documented
…and the documentation.
Digital files…
data maps, polarizations,
library comparisons, final decision
Dynamic Data Issues – Processing Perspective
35
Additional Tasks:
Pick Targets
Extract spatial data
Dynamic Data
36
Standard flow after
anomalies are selected…
Greater variability in
secondary polarizations, but
they are still sufficient to
identify TOI’s
Dynamic
Dynamic Stationary
Stationary
75mm
ISO’s
Construct a Library
QC Measured
Data
Invert and Look for Expected Munitions
Look for Unexpected Munitions
Prioritize
37
Technology Transfer - Workshops
Year Location ½ Day
1 Day
2 Days Brief/Workshop # Attendees
3/2010 Huntsville, AL x workshop 32
4/2010 Denver, CO x brief 2
12/2010 Washington, DC x brief 40
1/2011 Washington, DC x workshop 21
1/2011 Denver, CO x workshop 21
3/2011 Huntsville, AL x workshop 30
12/2011 Huntsville, AL x workshop 20
2/2012 Denver, CO x workshop 24
4/2012 Washington, DC x workshop 20
6/2012 Denver, CO x workshop 24
2/2013 Washington, DC x workshop 31
2/2013 Denver, CO x workshop 31
5/2013 Huntsville, AL x workshop 10
TOTAL 306
Representatives from over 40 firms
Technology Transfer, continued
38
Results and Final Processing
Products
39
Prioritized Dig List
The decision…
Digital files
data maps, polarizations,
library comparisons, metric matches
…and the documentation.
• Transparent
• Quantitative
• Documented