Transcript A Model-Based Approach to the Detection and Classification

Automated Detection and Classification Models
SAR Automatic Target Recognition Proposal
J.Bell, Y. Petillot
Contents
Automated Detection and Classification Models
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Background
ATR on SAR
ATR on Sonar
Supporting Technologies
Initial results on SAR
Way forward
ATR Approaches
Automated Detection and Classification Models
• Image Based techniques
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Based on large training sets
Assumes some form of linearity in the imaging process
image based only (difficult to fuse with other external data)
NN / Pattern Matching for classification
• Model Based techniques
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Model can be learned (trained) or imposed (CAD)
Can use physics
Can use simulation in the loop (test simulation vs data)
Can take into account non-linear image formation
Classical Approach
Automated Detection and Classification Models
Typical Recognition Scenario
Imaging
Platform
Target
Classifier
aˆ  T  72
Orientation
Estimator
ˆ  65
Model-Based Recognition
Automated Detection and Classification Models
Target Classifier
Orientation Estimator
aˆ  T  72
ˆ  65
pR ,A r , a - Conditiona l Data Model
p  A  a - Prior on Orientation (Know nor Simply Uniform)
p A a - Prior on Target Class (Know nor Simply Uniform)
Model-Based Recognition
Automated Detection and Classification Models
Training Data
Scene and Sensor
Physics
Image
Processing
Functional
Estimation
Lr a, 
Difficult
(and weak ?)
part
Inference
aˆ  T  72
Our proposal
Automated Detection and Classification Models
REMOVE FALSE ALARM
1
2
Detect ROIs
(MRF-based Model
Saliency
Context Detection)
YES
Extract
Highlight/Shadow
(CSS Model)
False
Alarm?
NO
Fuse Other Views
Import DTM Models
Target
Classify Object
Dempster-Shafer
YES
Positive
Classification?
NO
Our proposal
Automated Detection and Classification Models
Compare real
and simulated
image(section
3.3.1)
Segmentation
Markov Trees
Object
detection
Context
detection
(section 3.1)
Highlight/
Shadow
parameter
extraction
section(3.2)
Models
(section 3.3.2)
Generate set
of parameters
Simulate
image
Based on
parameters
And model
(section 3.4)
Sonar ATR
Automated Detection and Classification Models
• A Markov Random
Field(MRF) model
framework is used.
• MRF models operate well
on noisy images.
• A priori information can be
easily incorporated (priors).
• They are used to
retrieve the underlying
label field (e.g
shadow/non-shadow)
Basic MRF Theory
Automated Detection and Classification Models
A pixel’s class is determined by 2 terms:
– The probability of being drawn from
each classes distribution.
– The classes of its neighbouring pixels.
Incorporating A Priori Info
Automated Detection and Classification Models
• Object-highlight regions
appear as small, dense
clusters.
• Most highlight regions
have an accompanying
shadow region.
Segment by minimising:
U ( x, y, o)  s ( xs , ys )    st [1   ( xs , xt )]    ( xs , e2 ) ln X (s)    s ( xs , os )
sS
 s ,t 
sS
sS
Initial Detection Results
Automated Detection and Classification Models
DETECTED OBJECT
• Results are good (85-90% detection rate).
• Model sometimes detects false alarms due to clutter
such as the surface return – requires more analysis!
Object Feature Extraction
Automated Detection and Classification Models
• The object’s shadow is often extracted for
classification.
• The shadow region is generally more reliable than
the object’s highlight region for classification.
• Most shadow extraction models operate well on flat
seafloors but give poor results on complex seafloors.
The CSS Model
Automated Detection and Classification Models
• 2 Statistical Snakes segment the mugshot image into
3 regions : object-highlight, object-shadow and
background.
A priori information is modelled:
• The highlight is brighter than
the shadow
• An object’s shadow region can
only be as wide as its highlight
region.
CSS Results
Automated Detection and Classification Models
Standard Model
CSS Model
The Combined Model
Automated Detection and Classification Models
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Objects detected by MRF model are put through the CSS model.
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The CSS snakes are initialised using the label field from the
detection result. This ensures a confident initialisation each
time.
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The CSS can detect MANY of the false alarms. False alarms
without 3 distinct regions ensure the snakes rapidly expand,
identifying the detection as a false alarm.
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Navigation info is also used to produce height information which
can also remove false alarms.
Results
Automated Detection and Classification Models
Results 2
Automated Detection and Classification Models
Results 3
Automated Detection and Classification Models
Result 4
Automated Detection and Classification Models
Object Classification
Automated Detection and Classification Models
• The extracted object’s shadow can be used for
classification.
• We extend the classic mine/not-mine classification to
provide shape and dimension information.
• The non-linear nature of the shadow-forming process
ensures finding relevant invariant features is difficult.
Shadows from the same object
Modelling the Sonar Process
Automated Detection and Classification Models
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Mines can be approximated as
simple shapes – cylinders,
spheres and truncated cones.
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Using Nav data to slant-range
correct, we can generate
synthetic shadows under the
same sonar conditions as the
object was detected.
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Simple line-of-sight sonar
simulator. Very fast.
Comparing the Shadows
Automated Detection and Classification Models
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Iterative Technique is required to find best fit. Parameter
space limited by considering highlight and shadow length.
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Synthetic and real shadow compared using the Hausdorff
Distance.
H ( A, B)  max h( A, B), h( B, A)
h(a, b)  max min || a  b ||
aA
•
bB
It measures the mismatch of the 2 shapes.
HAUSDORFF
DISTANCE
Mono-view Results
Automated Detection and Classification Models
• Dempster-Shafer allocates a BELIEF to each class.
• Unlike Bayesian or Fuzzy methods, D-S theory can
also consider union of classes.
Bel(cyl)=0.83
Bel(sph)=0.0
Bel(cone)=0.0
Bel(clutter)=0.08
Bel(cyl)=0.0
Bel(sph)=0.303
Bel(cone)=0.45
Bel(clutter)=0.045
Bel(cyl)=0.42
Bel(sph)=0.0
Bel(cone)=0.0
Bel(clutter)=0.46
Multi-view Analysis
Automated Detection and Classification Models
Dempster-Shafer allows results from multiple views to be fused.
Mono-Image Belief
Fused Belief
Obj
Cyl
Sph
Cone
Clutt
Objs Cyl
Fused
Sph
Cone
Clutt
1
0.70
0.00
0.00
0.21
1
0.70
0.00
0.00
0.21
2
0.83
0.00
0.00
0.08
1,2
0.93
0.00
0.00
0.05
3
0.83
0.00
0.00
0.08
1,2,3
0.98
0.00
0.00
0.01
4
0.17
0.00
0.00
0.67
1,2,3,4
0.96
0.00
0.00
0.03
Multi-Image Analysis
Automated Detection and Classification Models
Mono-Image Belief
Fused Belief
Obj
Cyl
Sph
Cone
Clutt
Objs Cyl
Fused
Sph
Cone
Clutt
5
0.00
0.17
0.23
0.45
5
0.00
0.17
0.23
0.45
6
0.00
0.00
0.37
0.44
5,6
0.00
0.00
0.30
0.60
7
0.00
0.303
0.45
0.045
5,6,7
0.00
0.02
0.67
0.17
8
0.00
0.32
0.23
0.31
5,6,7,8
0.00
0.01
0.62
0.20
Context Detection
Automated Detection and Classification Models
The current detection model considers objects as a
Highlight/Shadow pair. An object can also be considered
as a discrepancy in the surrounding texture field.
The way Forward
Automated Detection and Classification Models
• Context Detection using segmentation based on
– Markov Random Fields
– Variational techniques
– Saliency
• Shape extraction for highlight / shadow
– Use of image formation process to force combined
meaningful extraction.
– Active contours to perform robust extraction (statistical
snakes, Mumford-Shah)
The way Forward
Automated Detection and Classification Models
• Model Based classification
– Initial model parameters extracted from segmentation
– Model is refined using
• A simulator of the image formation + search in parameter
space
• Direct inference using training and large databases
• Active Appearance models trained on large sets
• Robustify classification via
– Multi view combination
– Inclusion of DTM models
• via simulator
• via statistical priors?
Initial tests
Automated Detection and Classification Models
Image
Hierarchical MRF
Rayleigh
Segmentation
Two class segmentation (Variational approach)
Simulator
Automated Detection and Classification Models