Xu Huaping, Xu Huaping, Wang WeiWang Wei, Liu Xianghua, Liu XianghuaBeihang University, ChinaBeihang University, China

Introduction

SAR Image Segmentation with a MRF model

Fast Segmentation of SAR Imagery by Fusing Optical Imagery

Conclusions

Synthetic Aperture Radar (SAR) systems can acquire SAR imagery at all-climate, day and night. Image segmentation is an important technique of automatic interpretation of SAR imagery. However, the performance of SAR image segmentation would decline due to speckle noise.

Image segmentation based on the Markov Random Field (MRF) model attracts much attention for it adequately considers the tonal and textural characteristics of imagery. In spite of speckles, SAR image segmentation using the MRF model achieves a much better performance.

Different images can provide additional information which may improve the performance of image processing, especially when images of different sensors are combined. Therefore, SAR and optical imagery combination can be explored to improve image segmentation performance.

In this paper, we investigate to alleviate the time-consuming problem to carry out SAR image segmentation with a MRF model using simulated annealing algorithm. Two strategies are proposed for fast segmentation:

First, an optical image is applied to accelerate SAR image segmentation by selecting uncertain pixels which attend the SAR image segmentation.

Second, a fast annealing strategy is proposed to the simulated annealing algorithm to shorten the time consumed in optimization.

Suppose is the pixel intensities of a SAR image and its segmentation label field.

Image segmentation is to obtain the label field given the observed image . The maximum a posterior (MAP) probability method is adopted to achieve it:

The likelihood function and prior distribution need to be known.

{ , 1, , }iy y i N { , 1, }ix x i N

ˆ arg max ( | )

( | ) ( )arg max

( )

arg min[ log ( | ) log ( )]

xx P x y

p y x P x

p y

p y x P x

xy

Energy functionEnergy function

The observation model is the conditional distribution of the background clutter given the segmentation label field. Suppose all pixels obey independent identical distribution, then:

Rayleigh, Gamma and K distribution can be used to describe the observation model of SAR imagery.

Observation Model

1

( | ) ( | )N

i ii

p y x p y x

The random field is Markov random field iff: (1) (2)

If is a two-dimensional MRF, the prior model can be expressed as follows:

Prior Model

( )1( ) H xP x e

Z

{ }ix x

Fig.1 second-order neighborhood System

{ }ix x

( | ) ( | )ii S i i NP x x P x x

( ) 0P x

1

( ) ( ) 1i

N

i ji j

H x x x

The flowchart of SAR image segmentation with the assistant of one optical image is illustrated in Fig.2.

We artificially classify the optical image into three classes. Pick up a region of target pixels and a region of background pixels from the optical image, calculate their mean intensities:

Optical Image Classification

T

B

T opt( , )

B opt( , )

1( , )

1( , )

T

B

i j RR

i j RR

T s i jN

T s i jN

The simulated annealing algorithm with Gibbs sampler is employed to carry out image segmentation using a MRF model.

A label is defined as the predominant label if the label is shared by over half of its neighboring pixels:

Label update strategy

A Fast Annealing Strategy

{ : / 2}predom p ix p N N

Does

Fig.4 SAR image from TerraSAR-X Fig.5 Optical image from Quickbird Fig.6 Classification result of Fig.5

Fig.7 The result of Fig.4 Fig.8 The result of Fig.4 and Fig.5 Fig.9 The result of Fig.4 and Fig.5 with the fast annealing strategy

Segmentation results

Pixel number of wrong segmentation

Consuming time (s)

Fig.7 1191 9.469

Fig.8 538 4.250

Fig.9 339 1.985

Evaluation of Results

Two strategies are proposed to accelerate SAR image segmentation using a MRF model with the simulated annealing strategy.

The consuming time of image segmentation can be shortened by adding an optical image into segmentation or adopting the fast annealing strategy.

Better performance can be achieved by adding an optical image into image segmentation.