New Method for Ship Detection
Jian Yang Hongji Zhang
Dept. of Electronic Eng., Tsinghua Univ.
Yoshio Yamaguchi
Dept. of Inform. Eng., Niigata Univ.
Outline





Background
Polarization Entropy and Similarity Parameter
GOPCE based ship detection
Experiment Results
Summary
1. Background

Polarimetric Whitening Filter (PWF)

Novak, Burl

Identity Likelihood Ratio Test

DeGraff

HH HV VV RR LL Entropy Span

Touzi
Touzi’s work
Optimization of Polarimetric
Contrast Enhancement (OPCE)
h KA  g
t
max
h KB  g
t
subject to : g  g  g  1
2
1
2
2
2
3
h12  h22  h32  1
Ioannidis Hammers Kostinski
Boerner Yamaguchi Yang
Problem



Can we employ the OPCE for ship
detection?
It is easy to get the average Kennaugh
matrix of sea clutter, but how can we
get or construct the average Kennaugh
matrix of ships?
Can we extend the OPCE for ship
detection?
2 Polarization entropy and
similarity parameters
T11 T12 T13 
1

U H
T  T12* T22 T23   U 
2

*
*
T13 T23 T33 

3 
 1U1U1H  2U 2U 2H  3U 3U 3H
Pi  i
3
3

j 1
j
H    Pi log 3 Pi
i 1
Approximate expression
From the least square method and Vieta's Theorem
b
c
b2
AH  3.9408 2  7.5818 3  5.3008 4
a
a
a
a  Span  T11  T22  T33
b  T11T22  T11T33  T22T33  T12T12  T13T13  T23T23
c T
The average error:
1

 H  AH dk dk
1

2
 0.006
The Formula has a good approximation to the
theoretical value of the polarization entropy
Eigenvalues and logarithm are unnecessary!!!
Running Time by the proposed formula is
only 5% of that by the traditional approach
J. Yang, Y. Chen, Y. Peng, Y. Yamaguchi, H. Yamada,
“New formula of the polarization entropy”, IEICE Trans.
Commun., 2006, E89-B(3), 1033-1035
Similarity parameter
1
T
k
 shh  svv , shh  svv , 2shv 
2
single-look case
r (k1 , k2 ) 
0
1
H
(k ) k
0 2
1 2
k
k
0 2
2
0 2
2 2
Multi-look case
R(T1 , T2 ) 
0
1
0
2
T ,T


tr T


0 H
1
T20
 


0
tr
T
T

tr
T
T




F
F
2
J. Yang, et al., “Similarity between two scattering matrices,”
Electronics Letters, vol.37, no. 3, pp. 193-194, 2001.
T10
T20
0 H
1
0
1
0 H
2
Similarity between a target and a plate:
surface scattering
2
 1  
0
0
S HH  SVV
  
r1  r  k , 0   
2
2
0
0
0
  0   2 S HH  S VV  2 S HV
  


0
S HH
S
2
0
VV
2  Span

S HH  S VV
2

2
2  Span
Similarity between a target and a diplane:
Double-bounce scattering
 0 
0
0
S

S
HH
VV


r2  r  k , 1   
2  Span
 0 
  
2
Generalized OPCE (GOPCE)
based ship detection
h KA  g
t
max
h KB  g
t
subject to : g  g  g  1
2
1
2
2
2
3
h12  h22  h32  1
J. Yang, Y. Yamaguchi, W. -M. Boerner, S. M. Lin, “Numerical methods for
solving the optimal problem of contrast enhancement,” IEEE Trans. Geosci.
Remote Sensing, 2000, 38(2), pp. 965-971
GOPCE: Generalized OPCE
2


GP   xi ri   htm  K  g m
 i 1

3
2
 3

t
x
r

h

i
i
m  K A  gm



max  i 1
2
 3

t
  xi ri   h m  K B  g m
 i 1

r1  r ([ S ], diag (1,1)) 
2
0
0
sHH
 sVV
2( s
r2  r ([ S ], diag (1, 1)) 
2
0
HH
0 2
VV
s
2 s
0
0
sHH
 sVV
2
2
0 2
HV
)
2
0
0
0
2 ( sHH
 sVV
 2 sHV
2
)
r3 : Cloude entropy
J. Yang, et al., “Generalized optimization of polarimetric contrast
enhancement”, IEEE GRSL., vol.1, no.3, pp.171-174, 2004
GOPCE based ship detection
2
 3

GP   xi ri   htm  K  g m
 i 1

For a sea area
2
 3

min Var (  xi ri   htm  K  g m )
 i 1

subject to:
x12  x22  x32  1
Average Kennaugh matrix of ships




the scattering contributions of a ship
direct reflection of plates
double reflections of diplates of the ship
some multi-reflections of the surface of
the ship, or some multi-reflections
between the ship and the sea surface
 K (TA)   a  K  plate  b  K (0)diplate  c  K ( )diplate  d  K multi
For example : a  0.4, b  0.5, c  0.1, d  0,  450
Experimental results
NASA/JPL AirSAR over
Sydney coast,
Australia.
Span image
Experiment results
 K (TA)   a  K  plate  b  K (0)diplate  c  K ( )diplate  d  K multi
a  0.4, b  0.5, c  0.1, d  0,  450
2
 3

GP   xi ri   htm  K  g m
 i 1

gm  (1, 0.9453, -0.1497, 0.2897)t
hm  (1, -0.6313, 0.2925, 0.7182)t
xm  (-0.4000, -0.2750, 0.8743)t
Experiment results
Power Image by OPCE
Experiment results
GP Image by GOPCE
Experiment results
Filtered result by PWF
Detection results:
false alarm rate 1%
span
OPCE
PWF
GOPCE
5. Summary
(1) OPCE has been developed
(2) GOPCE is effective for ship detection
Thank you!
yangjian_ee@tsinghua.edu.cn
Speckle Filtering

Speckle phenomenon in SAR/POLSAR
Observation Point
Surface Roughness
Scattering from distributed scatterers
Coherent interferences of waves scattered from
many randomly distributed scatterers in the
resolution cell
Granular Noise
Speckle Phenomenon
24/28
Speckle Filtering

Challenge of speckle filtering
Speckle Filtering
Speckle Reduction
These two
objectives should
be achieved
simultaneously
Detail Preservation
25/28
Pre-test Approach for Speckle Filtering

Classical Methods for Speckle Filtering

Boxcar Filter
3*3 boxcar

To-be-filtered pixel
Pixel selected for averaging
MMSE Lee’s filter with edge detector
8-direction edge detectors
26/28
Pre-test Approach for Speckle Filtering
Summary of Speckle Filtering : Two-Step Methodology
Selecting homogenous pixels
Boxcar
Lee
Pre-test
local
area
Averaging homogenous pixels
In neighboring area
Uniformly weight
In aligned matching window
By mmse criteria
In non-local area
patch : pixel itself and its
local neighboring
By the similarity of patch
To-be-filtered
pixel
patch
Pre-tested pixels
non-local area : other than
neighboring area
pre-test : selecting homogenous
pixels in non-local area by patch
An example of pre-testing homogenous
pixels in non-local area by patch
27/28
Pre-test Approach for Speckle Filtering

Non-local but homogeneous pixels using proposed
method
28/28
Pre-test Approach for Speckle Filtering

For each pixel
xi , yi
- For each pixel
xj , yj
in non-local searching area
1. Calculate the similarity test Ti , j
between the patches with xi , yi and
x j , y j as the center respectivel
2. If test Ti , j > threshold, accept
x j , y j as the homogenous pixel
to xi , yi , and calculate the weight
3. Average homogenous pixels with
their normalized weight to get
filtered covariance matrix
Calculate the
similarity
between 2
patches
xj , yj
xi , yi
patch
searching area of xi , yi
29/28
Pre-test Approach for Speckle Filtering

Experimental Results
(a) Original
(b) Refined Lee
SAR-Convair 580 C-band
Image size : 340*220
Resolution : 6.4m*10m
(c) Pre-test
30/28

C-band AirSAR data
SAN Francisco area
(a)Original
(b)Boxcar
(c)Refined Lee
(d)Pre-test
4 multi-look
Image size :
300*300
Resolution :
About 10m*10m
31/28