New Method for Ship Detection Jian Yang Hongji Zhang Dept. of Electronic Eng., Tsinghua Univ. Yoshio...

New Method for Ship Detection

Jian Yang Hongji ZhangDept. of Electronic Eng., Tsinghua Univ.

Yoshio YamaguchiDept. 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)

maxt

At

B

K

K

h g

h g

Ioannidis Hammers Kostinski Boerner Yamaguchi Yang

2 2 21 2 3 1h h h

2 2 21 2 3: 1subject to g g g

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

11 12 13 1* H

12 22 23 2* *

13 23 33 3

H H H1 1 1 2 2 2 3 3 3

T T T

T T T

T T T

T U U

U U U U U U

3

31

logi ii

H P P

3

1i i j

j

P

Approximate expressionApproximate expression

11 2 0.006H AH dk dk

From the least square method and Vieta's Theorem2

2 3 43.9408 7.5818 5.3008

b c bAH

a a a

11 22 33

11 22 11 33 22 33 12 12 13 13 23 23

a Span T T T

b T T T T T T T T T T T T

c

T

The average error:

The Formula has a good approximation to the The Formula has a good approximation to the theoretical value of the polarization entropytheoretical 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

1, ,2

2

T

hh vv hh vv hvk s s s s s Similarity parameterSimilarity parameter

20 H 01 2

1 2 2 20 01 22 2

( )( , )r

k kk k

k k

single-look case

Multi-look case

H0 00 01 21 2

1 20 0 H H0 0 0 0

1 2F F 1 1 2 2

tr,( , )

tr trR

T TT TT T

T T T T T T

J. Yang, et al., “Similarity between two scattering matrices,” Electronics Letters, vol.37, no. 3, pp. 193-194, 2001.

2 2 20 0 0HH VV HV

20 0HH VV

1

2 20 0HH VV HH VV

1

, 020

2

2 2

S

S S S

S S

Sr r

S S

Span Span

k

Similarity between a target and a plate:surface scattering

Similarity between a target and a diplane:Double-bounce scattering

20 0HH VV

2

0

, 1

02

S Sr r

Span

k

Generalized OPCE (GOPCE) based ship detection

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

maxt

At

B

K

K

h g

h g

2 2 21 2 3 1h h h

2 2 21 2 3: 1subject to g g g

20 0

1 2 2 20 0 0([ ], (1,1))

2( 2 )

HH VV

HH VV HV

s sr r S diag

s s s

20 0

2 2 2 20 0 0([ ], (1, 1))

2( 2 )

HH VV

HH VV HV

s sr r S diag

s s s

3 :r Cloude entropy

GOPCEGOPCE: : GGeneralized eneralized OPCEOPCE

23

1

ti i m m

i

GP x r K

h g

23

123

1

max

ti i m A m

i

ti i m B m

i

x r K

x r K

h g

h g

J. Yang, et al., “Generalized optimization of polarimetric contrast J. Yang, et al., “Generalized optimization of polarimetric contrast enhancement”, IEEE GRSL., vol.1, no.3, pp.171-174, 2004enhancement”, IEEE GRSL., vol.1, no.3, pp.171-174, 2004

GOPCE based ship detection

23

1

ti i m m

i

GP x r K

h g

23

1

min ( )ti i m m

i

Var x r K

h g

For a sea areaFor a sea area

subject to: subject to: 123

22

21 xxx

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 ( ) (0) ( )

plate diplate diplate multiK TA a K b K c K d K

0: 0.4, 0.5, 0.1, 0, 45For example a b c d

Experimental results

NASA/JPL AirSAR over Sydney coast, Australia.

Span image

Experiment results

(1, 0.9453, -0.1497, 0.2897)tm g

( ) (0) ( )plate diplate diplate multi

K TA a K b K c K d K

00.4, 0.5, 0.1, 0, 45a b c d

23

1

ti i m m

i

GP x r K

h g

(1, -0.6313, 0.2925, 0.7182)tm h

(-0.4000, -0.2750, 0.8743)tm x

Experiment results

Power Image by OPCE

Experiment results

GP Image by GOPCE

Experiment results

Filtered result by PWF

span PWF

OPCE GOPCE

Detection results: false alarm rate 1%

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

Scattering from distributed scatterers

Coherent interferences of waves scattered from many randomly distributed scatterers in

the resolution cell

Granular Noise

Speckle Phenomenon

Observation Point

Surface Roughness

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Speckle Filtering Challenge of speckle filtering

Detail PreservationSpeckle Reduction

Speckle Filtering

These two objectives

should be achieved

simultaneously

These two objectives

should be achieved

simultaneously 25/28

Pre-test Approach for Speckle Filtering

Classical Methods for Speckle Filtering Boxcar Filter

MMSE Lee’s filter with edge detector

3*3 boxcarTo-be-filtered pixel

Pixel selected for averaging

8-direction edge detectors

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Pre-test Approach for Speckle Filtering

Selecting homogenous pixels Averaging homogenous pixels

Boxcar localarea

In neighboring area Uniformly weight

Lee In aligned matching window By mmse criteria

Pre-test In non-local area By the similarity of patch

To-be-filteredpixel

Pre-tested pixels

Summary of Speckle Filtering : Two-Step MethodologySummary of Speckle Filtering : Two-Step Methodology

An example of pre-testing homogenous pixels in non-local

area by patch

patch

patch : pixel itself and itslocal neighboring

non-local area : other than neighboring area

pre-test : selecting homogenouspixels 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 - For each pixel in non-local searching area

1. Calculate the similarity test between the patches with and

as the center respectivel2. If test > threshold, accept as the homogenous pixel to

, and calculate the weight3. Average homogenous pixels with their normalized weight to get filtered covariance matrix

,i jT

,i ix y

,j jx y

searching area of ,i ix y

,i ix y

,j jx y

,i ix y,j jx y

,i ix y

,j jx y

Calculate the

similarity between 2

patches

patch

,i jT

29/28

Pre-test Approach for Speckle Filtering

Experimental Results

(a)Original (b) Refined Lee (c) Pre-test

SAR-Convair 580 C-bandImage size : 340*220

Resolution : 6.4m*10m

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*300Resolution :

About 10m*10m

31/28