igarss2011.ppt

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Information extraction from SLC SAR images

Dušan Gleich

University of Maribor, Faculty of Electrical Engineering and Computer ScienceLaboratory for Signal Processing and Remote ControlMaribor, Slovenia

IGARSS 2011, 24-29 July 2011

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Outline of presentation

Motivation

Maximum A Posteriori (MAP) despeckling using Auto-Binomial model (ABM)

Non-quadratic regularization and ABM

Experimental results

Conclusion

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SLC TerraSAR-X Spotlight image, 10976×6056 pixels, 0.8×1.1 m (range×azimuth) resolution

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Motivation: Despeckling and information extraction using texture model

Original image, 1024×1024 pixels

Despeckled image

Texture parameters

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Y = X n, Y- image, X-noise “free” image, n – speckle

Maximum A Posteriori (MAP) filtering can be implemented using a posteriori density function conditional to the observation

find MAP estimate:

Maximize evidence to estimate texture parameters

SAR image despeckling – model based approach

-| 1- ii

i i ii

G G xxp xx

( | , ) ( | )( | , ) ( | , ) ( | )

( | )

p y x p xp x y p y x p x

p y

2 1 2

( | ) 2 exp( )

L Ly L yp y x L

x x L x

log ( | , ) 0p x yx

Original Amplitude

SARimage

Denoised image

1. Order BayesianInference

(MAP)

Image and noise

models

2. Order Bayesian inference

M. Hebar at all, ‘Auto-Binomial Model for SAR Image Despeckling and Information Extraction ,’’ IEEE Geoscience and Remote sensing,” August 2009.

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Auto-binomial model

-| 1- ii

i i ii

G G xxp xx

1

1 exp( )

( )

s

i

i j i ji j

j N

x xa b

G

1=[-1.5,-1.5]

2=[ 1.06, 0.79, 0.64] =[ a, b1, b2, b3, b4, ..]

Model order 1 Model order 2

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ABM model

5=[0.01, 0.01, -0.01, -0.01, 0.01, -0.41, 1.4, 1.4, 0.41, 0.11, 0.01, 0.1]

3=[1.2, 1.2, 0.7, -0.7, 1.3, 1.3]

4=[0.2, -0.2, 0.5, -0.5, -0.1, -0.1, 1, 1, 1, 1]

•Constant model order 4•Window size of 30×30 pixels

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Evidence maximization

Change Texture

parameters

Compute evidence

Computeevidence

ComputeMAP estimate

with initial

Max. evidence

ComputeMAP estimatewith estimated

Yes

No

2

3

-2 2 log log 0

1 1-

- i i i

i i i

y y G xL Lx x x

1

1ˆ ˆlog ( | ) (log 2 log ) log ( | ) log ( | )

2

N

ii ii

p y h p y x p x

2

2 41

2 1 16

1

N N

ii

i i i i i

yLh L

x x G x x

log ( | , ) 0p x yx

-| 1- ii

i i ii

G G xxp xx

1

1 exp( )

( )

s

i

i j i ji j

j N

x xa b

G

=[ a, b1, b2, b3, b4, ..]

1

1ˆ ˆlog ( | ) (log 2 log ) log ( | ) log ( | )

2

N

ii ii

p y h p y x p x

2

2 41

2 1 16

1

N N

ii

i i i i i

yLh L

x x G x x

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Result: MAP estimate (despeckled image) and texture parameter

1.

1.

2.3.

4.

2.

3.

4.

5.

6.

5.

6.

despeckled image despeckled imagedespeckled image synthetic texture synthetic texture

MAP Estimate Real images and synthetic generated form texture parameters

(a) (b) (c)

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Original image

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Despeckled image

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Classified texture parameters with K-means

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Complex SAR image: non-quadratic regularization

ˆ arg min ( )x

x J x

2 21 2( )

k k k

k k kJ x y nx x Dx

2 21 2( ) ( )

kk k

r s r s rk kr k

J x y nx x x Dx

M. Cetin and W.C.Karl, ‘’Feature-Enhanced Synthetic Aperture Radar Image Formation Based on Nonquadratic Regularization,’’ IEEE Tran. Image Processing, April 2001

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Proposed method: Tikhonov optimization

1( 1) ( ) ( ) ( )( ) ( )n n n nx x H x J x

2 21 1 2 2( ) 2 ( ) ( ) ( ) ( )T T TH x N N k x k x D x D x

( ) ( ) 2J x H x x Ty

1 1 /22

1

( )

k

i

A diagx

2 1 /22

1

k

i

A diagx

( ) exp( ( )) ix diag j x

2 21 2( ) ( )

kk k

r s r s rk kr k

J x y nx x x Dx

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Parameter determination/estimation

Regularization parameters k, 1 and 2

4 Evidence maximization4 M. Soccorsi at all,” Huber-Markov Model for Complex SAR Image

Restoration,” IEEE GRSL, January 2010

Texture parameters

4 Evidence maximization

Window sizes

4 Evidence maximization (30×30) and Matrix order (3×3)

Despeckled image

Tikhonovoptimization

Image modeling

Evidence maximization

Original SAR image

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Objective experimental results

T1(µ=130.4) MSE Mean(x) ENL

MBD 209.4 128.60 2.457

Lee ref. 414.6 127.54 2.42

ABM+Tikh 226.1 129.18 2.89

T1(µ=123.1) MSE Mean(x) ENL

MBD 208.43 121.67 4.19

Lee ref. 538.40 124.27 5.88

ABM+Tikh 268.44 123.03 6.56

2 2ENL

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Experimental results

Original ABM+ Tikhonov optimization

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Experimental results

MAP-ABM ABM+ Tikhonov optimization, 6th iteration

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Texture parameters

ABM texture parameters

ABM+ Tikhonov optimization

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Experimental results: Original image

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Despeckled image: complex

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Despeckled image: MBD

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Classified texture parameters (K-means)

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Classified texture parameters (K-means) - MBD

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Changes in real, imaginary parts and phase

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Interfeormetromerty – phase preservation

Interferogram form original pair Interferogram from despeckled pair

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Computation complexity

C++, Intel optimized compiler

4 MBD: total 345 seconds, image 1024×1024, est.window 16×164 ABM+T: total 440, est. window 16×16

CUDA, Geforce 9600

4 MBD: 28s4 ABM+T: 56s

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Conclusion

Weakness:

– Computationally very demanding algorithm

– Evidence maximization– Manual determination of window sizes– Manual choosing model order or ABM

Tikhonov optimization well

4 preserves point features4 preserves phase

Texture parameters evaluation

Larger window sizes (128×128 pixels)