igarss2011.ppt
31
Institute of automation, University of Maribor 1/31 Information extraction from SLC SAR images Dušan Gleich University of Maribor, Faculty of Electrical Engineering and Computer Science Laboratory for Signal Processing and Remote Control Maribor, Slovenia IGARSS 2011, 24-29 July 2011
Transcript of 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)
