classification of high resolution sar images using textural features

8/6/2019 Classification of high resolution sar images using textural features

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Aurlie Voisin, Vladimir A. Krylov, and Josiane Zerubia


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Synthetic Aperture Radar (SAR): active sensor used fordifferent kinds of applications: Security, Epidemiology, Environment, Risk management

Very high resolution (VHR) SAR data (up to 1 meter) Speckle [Oliver 04]

Heterogeneity [Cheney 09]

Supervised classification of VHR single-channel SARamplitude images Methods : neural networks [Jacob 02], bags-of-features [Yang 09], etc.

Proposed method: statistical modeling + Markov random fields (MRFs)

2


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3

Supervised classification

1 learning image

1 test image

M classes (e.g.: water, vegetation, urban)

Original SAR

amplitude image


4/24

4

Learning

Test

PDF SAR amplitude of

each class

MRF parameter

estimation

Energy minimization to

find the class of the test-image pixels


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5

Learning

Test


each class

MRF parameter

estimation




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Dictionary-based Stochastic Expectation Maximization

Heterogeneity of SAR images

Single probability density function (PDF) doesnt accurately model

SAR amplitude statistics

Model the SAR amplitude PDF by a FMM (Finite Mixture Model)

6

K

k

kkk rpPrp1

)(.)(

[Moser 10]


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Estimation of:

K: number of components

pk(r| k): kth PDF family component

Parameters of the kth component (k and Pk)

with

Data incompleteness

belongs to which statistical population?

Unsupervised context

7

K

k

kP

1

1 10 kP

Nrrr ,...,1

K

k

kkk rpPrp1

)(.)(


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Log Normal

Weibull

Fisher

Generalized Gamma

Nakagami

K-Root

Generalised Gaussian Rayleigh

Heuristic

Theoretical

2

))(ln(

12

1

),(

mr

ermrf

r

errf1

2 ),(

ML

L

MLr

M

Lr

M

L

ML

MLMLrf

)1(

)(

)()(

)(),,(

1

3

r

er

rf

1

4)(.

),,(

12

5)(

2),(

Lr

L

L

erL

LLrf

2/1

12

6 2)()(

4),,(

LMrKr

LM

MLMLrf LM

ML

ML

der

rfr

2/

0

)sin()cos(

7

/1/1/1

)(

),(

09/21/2010 SPIE Remote Sensing Toulouse 2010 - A. Voisin et al.

[Moser 06]

[Krylov 09]


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E step:

S step: stochastic labeling step leading to

M step:

9

K

l

t

lil

t

l

t

kik

t

kt

ik

rpP

rpP

1

),(.

),(.

t

ikw

N

i

kik

t

ik

t

krpw

k 1

1),(lnmaxarg

imagesize

kpixelsnbP

t

k_

_1

[Celeux 95]


10/24

ML not feasible for some PDF distributions

Method ofLog-Cumulants [Moser 06, Tison 04]:

Mellin transform of the PDF [Sneddon 72]

th order second kind cumulant

10

01).())(()( duuupspMs suuu

)1())(ln()(v

uv Family MoLC equations

Log-Normal 1=m, 2=

Weibull 1=ln()+(1)/, 2=(1,1)/

Nakagami 21=(L)-ln(L/), 42=(1,L)

Generalized Gamma 1=(k)/+ln(), 2=(1, k)/

3=(2, k)/3


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E - step: (posterior proba)

S step: sample the label for each greylevel according to

MoLC step:

K step: for k=1,,Kt, if Pk

t+1 < threshold, eliminate the kth

component. Kt+1 = Kt-1

Model Selection step: for each k, compute the log-likelihood,

and define pkt+1(.) as the PDF yielding the highest value

11

tK

l

t

l

t

l

t

l

t

k

t

k

t

kt

k

zpP

zpP

1

),(.

),(.

)(zst

ktQz

t

kjj

t

kj zfzhL )(ln).(

t

k

1,...,0 Zz

1

0

1

)(

)(

Z

z

Qzt

k

zh

zh

P kt

kt

kt

Qz

Qzt

kzh

zzh

)(

)ln().(1

1

kt

kt

Qz

Qz

bt

k

t

bkzh

zzh

)(

))).(ln(( 1

kzszQt

kt )(:

[Moser 06]

[Krylov 09]

3,2b


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Initialization Kmax = 6 chosen by trial and error

Randomly chosen labels

For each iteration t, the global log-likelihood is

computed, if it is > (max log-likelihood), the parameters

are saved

Stopping criterion

Maximum number of iterations reached

Number of components = 1

12


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Model KS distance

GGamma 0.022

DSEM 4: GGamma,

Naka, LogN, Weib.

0.007

TerraSAR-XimageofRoshe

nheim

(Germany)(Infoterra,2008)

COSMO-Skymedimageof

Cavallermaggiore(It

aly)

(ISA,2008)

Model KS distance

Weibull 0.053

DSEM 4: LogN,

Naka (2), Weib.

0.011

Statistics of the

whole imageStatistics of the

vegetation class


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Learning

Test


each class

MRF parameter

estimation




15/2415

MRF: robustness against speckle and contextualinformation [Besag 86, Dubes 89, Fjortoft 03]

Anisotropic second-order neighborhood system

Gibbs distribution[Besag 74, Geman 84]

: Localcharacteristic (conditional proba) for each class m

Potts model:

Pseudo-logLikelihood

M;1

M

j

xxH

xxH

s

s

mss

ms

s

mss

js

sms

e

e

xP

xxPxxPxp

1

),(

),(

)(

)(

)()(

)(

)(

,)()(

Ss Csss

xxs

s ssPDFxxH

',:'

)(

'.)ln(),,(

scliques

s

ms xpxPL_

)()(ln))(ln(


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Problematic: Single pol. CSK images

Find a 2nd channel to improve the accuracy

Textural features

COSMO-Skymedimageof

Cavallermaggiore(Ita

ly)(ISA,2008)

Semi-Variogram [Chen 04]

Grey-Level Co-occurrence

Matrix (GLCM) variance

[Haralick 73]


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PDF SAR amplitude ofeach class and each

channel

Copulas

MRF parameter

estimation


find the class of the test-

image pixels

Learning

Test

[Moser 10]


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PDF SAR amplitude ofeach class and each

channel

Copulas

MRF parameter

estimation


find the class of the test-

image pixels

Learning

Test

[Moser 10]

21

2211

*

2211

)(),(

)().()( yy

yFyFC

ypypypmmmmm

mmmmmm


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19

Water Urban Vegetation Overall

DSEM-MRF 99.14 % 98.88 % 84.65 % 94.22 %

K-NN-MRF 96.72 % 96.09 % 99.92 % 97.58 %

CoDSEM (Semivar.) 98.37 % 98.91 % 100 % 99.09 %

CoDSEM (GLCM) 98.62 % 98.42 % 100 % 99.01 %

DSEM-MRFCoDSEM (GLCM)

COSMO-SkymedimageofCavallermaggiore

(Italy)

(ISA,2008)


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20


DSEM-MRF 92.95 % 98.32 % 81.33 % 90.87 %

K-NN-MRF 90.56 % 98.49 % 94.99 % 94.68 %

CoDSEM (GLCM) 91.28 % 98.82 % 93.53 % 94.54 %

CoDSEM (GLCM) K-NN-MRF

TerraSAR-X image of Rosenheim

(Germany) (Infoterra, 2008)


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DSEM-MRF 99.81 % 99.03 % 99.99 % 99.61 %

CoDSEM (GLCM) 98.66 % 99.56 % 99.27 % 99.16 %

CoDSEM (GLCM)

COSMO-Skymedimage of Port-au-Prince

(Haiti) (ISA, 2009)

DSEM-MRF


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22

Algorithm validated in the application:

urban/land/water separation on several single-pol. SAR

images

Smoothing effects at spatial borders

Possible improvements:

More sophisticated texture-extraction techniques

Taking into account urban geometry via hierarchical/multiscaleMRFs


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23

French defense agency (DGA)

Italian Space Agency (ISA)

Infoterra (Astrium Services)

Dr. G. Moser and Prof. S. Serpico from Univ. of Genoa

for their helpful comments and fruitful collaboration


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[Oliver 04] Oliver, C. and Quegan, S., [Understanding Synthetic Aperture Radar images], SciTech Publishing (2004).

[Jacob 02] Jacob, A. M., Hemmerly, E. M., and Fernandes, D., SAR image classification using a neural classifier based on Fisher criterion, in

[Medical Imaging: Image Processing], Proc. of the VII Brasilian Symposium on Neural Networks (SBRN), 168172 (2002).

[Yang 09] Yang, W., Dai, D., Triggs, B., and Xia, G.-S., Semantic labeling of SAR images with hierarchical Markov aspect models, HAL Research

Report , hal00433600 (2009).

[Cheney 09] Cheney, M. and Borden, B., [Fundamentals of radar imaging], Phladelphia: Society for industrial and applied mathematics (2009).

[Moser 10] Moser, G., Krylov, V., Serpico, S. B., and Zerubia, J., High resolution SAR image classification by Markov random fields and finite

mixtures, Proc. of SPIE7533, 753308 (2010).

[Moser 06] Moser, G., Serpico, S. B., and Zerubia, J., Dictionary-based Stochastic Expectation Maximization for SAR amplitude probability

density function estimation, IEEE Trans. Geosci. Remote Sens. 44(1), 188199 (2006).

[Krylov 09] Krylov, V., Moser, G., Serpico, S. B., and Zerubia, J., Dictionary-based probability density function estimation for high-resolutionSAR data, Proc. of SPIE7246, 72460S (2009).

[Celeux 95] Celeux, G., Cheveau, D., and Diebolt, J., On stochastic versions of the EM algorithm, INRIA Research Report2514 (1995).

[Tison 04] Tison, C., Nicolas, J.-M., Tupin, F., and Maitre, H., A new statistical model for Markovian classification of urban areas in high-

resolution SAR images, IEEE Trans. Geosci. Remote Sens. 42(10), 20462057 (2004).

[Sneddon 72] Sneddon, I., [The use of integral transforms], McGraw-Hill, New York (1972).

[Fjortoft 03] Fjortoft, R., Delignon, Y., Pieczynski, W., Sigelle, M., and Tupin, F., Unsupervised classification of radar images using hidden

Markov chains and hidden Markov random fields, IEEE Trans. Geosci. Remote Sens. 41(3), 675686 (2003).

[Besag 86] Besag, J., On the statistical analysis of dirty pictures,Journal of the Royal Statistical Society48, 259

302 (1986).[Dubes 89] Dubes, R. C. and Jain, A. K., Random field models in image analysis,Journal of Applied Statistics 16(2), 131164 (1989).

[Besag 74] Besag, J., Spatial interaction and the statistical analysis of lattice systems,Journal of the Royal Stat. Soc. 36(2), 192236 (1974).

[Geman 84] Geman, S. and Geman, D., Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Patt. Anal.

Mach. Intell. 6(6), 721741 (1984).

*Chen 04+ Chen, Q. and Gong, P., Automatic variogram parameter extraction for textural classification of the panchromatic IKONOS imagery,

IEEE Trans. Geosci. Remote Sens. 42(5), 11061115 (2004).

[Haralick 73] Haralick, R. M., Shanmugam, K., and Dinstein, I., Textural features for image classification, IEEE Trans. on Systems, Mans and

Cybern. 3(6), 610

621 (1973).

classification of high resolution sar images using textural features

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