Sar Image Segmentation Using Multi-objective Optimisation Technique

Presented by ~

INDRANATH CHATTERJEE

[email protected]

Department of Computer Science

University of Delhi

January 18, 2015 University of Delhi 1

What is Image Segmentation? Extraction of the important objects from an input

image.

Partitions an image into distinct regions containing each pixels with similar attributes.

Partitions a digital image into multiple segments and classify them into distinct classes.


Why to use Image Segmentation?

The goal of image segmentation is to cluster pixels into salient image regions.

It is used mainly for:

Computer Vision

Medical Imaging

Object Recognition task

Content based Image Retrieval

and many more


Type of Image Segmentation

Edge Based

Color Based

Texture Based

Disparity Based

Motion Based


Image Segmentation in Remote Sensing It becomes more important in the field of remote sensing

for Geo-Spatial satellite images.

Mainly two types of images are taken by remote sensing purposes for segmentation:

SPOT (Satellite Pour l'Observation de la Terre)- Taken by SPOT satellite with HRV and HRVIR sensor detectors.

SAR (Synthetic Aperture Radar)- Taken with the help of radar signals.


Example of SAR and SPOT Image


Fig 1. SAR Image Fig 2. Multi-spectral SPOT Image

SAR Image Obtained from ERS, JERS and RADARSAT satellites.

It can works in any climatic condition, day and night.

Creates 2D/3D representation of terrain objects with the help of radar signal and its echo.

Mounted on a moving platform such as an aircraft or spacecraft.

Formed by coherent interaction of the transmitted microwave with the targets. Hence, it suffers from the effects of speckle noise.


Speckle Noise Arises from coherent summation of the signals

scattered from ground scatterers distributed randomly within each pixel.

Granular 'noise' that inherently exists in and degrades the quality of the SAR images.

In SAR, it is a multiplicative noise, i.e. it is in direct proportion to the local grey level in any area.


Research Problem

SAR Image Segmentation and classification to find out different

regions within the image and analyze them.


Research Objective To make images noiseless.

Detail Preserving Segments.

Fast Computation Time of segmentation.

Thus it needs Multi-Objective Optimizationtechniques to solve the issues.


Previous Work Different single objective techniques of SAR image

segmentation have been proposed, for example, clustering algorithm, threshold methods, graph-based approaches, etc.

In almost all the works, images were either properly segmented removing less speckle noise or removed noise but failed to segment the image efficiently. The optimal value of the threshold for each criterion does not produce a satisfactory image segmentation.

To obtain both criteria of noise removal and proper image segmentation, Multi-Objective optimization approach has been taken.


Multi-Objective Optimization An area of multiple criteria decision making, that is

concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

The goal may be to find a representative set of Non-dominated Pareto optimal solutions.

In mathematical terms, a multi-objective optimization problem can be formulated as:

where k>=2.


Multi-Objective Approaches Recently, various approaches are taking part for

segmenting SAR image with multi-objective optimization techniques.

Several authors have made their contribution towards the optimization of the problem.

Several multi-objective approaches like using ant colony optimization, artificial bee colony algorithm, particle swarm algorithm, evolutionary algorithm, genetic algorithm, artificial immune algorithm with fused complimentary features, quantum inspired evolutionary algorithm, etc.


Surveyed several recent research work for multi-objective optimization algorithms.

Here , we will study on some of the different research work on the same problem.


1. Fast SAR Image segmentation Algorithm based on PSO and Grey Entropy.(Ma, Zhang, Tian, Lu- 4th ICNC08)


1.1 Introduction PSOGE algorithm used here, which is based on

particle swarm optimization and grey entropy for speed up.

Technique of pbest and gbest to find the threshold value efficiently.

Grey-level co-occurrence matrix is constructed from SAR Image. On the basis of the matrix, a grey entropy based fitness function is designed for PSO.

Objective is to minimize speckle noise and efficient segmentation .


Particle Swarm Optimization Eberhart and Kennedy proposed the PSO method in 1995.

It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution.

Moving random particles around in the search-space altering it's position and velocity.

Movement is influenced by its local best known position but also guided toward the best known positions in the search-space, updated as better positions are found by other particles. This moves the swarm toward the best solutions.


1.2 Methodology Filtered and Gradient image are extracted from SAR image

to analyze via grey-level information.

Grey-level co-occurrence matrix is formed. It correctly reflect the grey-level distribution among both homogenous and texture region.

New co-occurrence matrix is formed from F (blurred filtered image) and G (sharp gradient image) of I (original image).

By circular averaging filter.January 18, 2015 University of Delhi 18

Probability co-occurrence matrix c_matrix pi,j :

Two thresholds in images F and G are s and t , which are grey numbers ranging from 0 to L, then the position of (s, t) will divide the grey-level co-occurrence matrix:


Finally, a grey entropy is utilized to act as the object function in locating the best segmentation threshold by building the fitness function for the PSO.

f = -GE(s,t) GE=Grey-entropy

To locate (s, t) is a time-consuming work, so here we would like to introduce the PSO to quicken the searching speed by altering Particles movement and velocity by:


1.3 PSOGE Algorithm flowchart


1.4 Experiment & Results Experiment made with real 256X256 SAR image and

applied PSOGE over it.

Image given below was taken with lot of speckle noise.

Fig 2 is the segmented image from SAR image in Fig 1.

It took processing time of 14.0825 seconds with (s, t)=(74, 24),


1.5 Conclusion To improve the segmentation quality in both speed and

effect, this paper suggests a PSOGE algorithm which is quite good.

Information of homogeneous regions, and original details are there in co-occurrence matrix Hence, the PSOGE is free from the influence of speckle.

PSOGE may avoid premature stop by adjusting some parameters in PSO such as large values of the number of particles or evolution generations, or the maximum flying speed.


2. SAR Image Ship Detection Based on Ant Colony Optimization.(Li, Wang 5th Int. CISP12)


2.1 Introduction Edge detection is an important method of extracting image

edge for image segmentation. Here ship detection is performed from the SAR images.

Ship detection is performed by Ant Colony Optimization(ACO) technique which adjusts threshold on edge detection dynamically.

Here ACO is applied in contrast with traditional edge detection algorithms and WT method.

ACO is much more efficient in reduced computing time and complete edge detection with speckle noise in SAR image than other methods.


2.2 Ant Colony Optimization M.Dorigo etc have made a introduction to a general

optimization technology--Ant Colony Optimization.

It is a probabilistic technique for solving computational problems to find good paths through graphs.

Ants leave pheromones after their route in athletic process.

By perceiving the existence and strength of the pheromone, ants move in the direction of the higher strength.

The more the ants choose the path, the greater probability the other ants choose. Through communication of the information the ant individual chooses the optimal path, and finally searches the target.


2.3 Methodology In this paper the pheromone matrix is updated twice, and

the computing time and cost are greatly reduced through dynamic adjustment of the threshold optimization algorithm.

Step 1: Initialization Process:

Firstly K ants are randomly assigned on an M1 M2 image. The initial value of each pheromone matrix component (0) is set to be constant (init) .


Step 2: Construction Process

During construction process, one ant is randomly selected from K ants, and move on the image for L movement steps from the node (l,m) to its neighbouring node (i, j) according to the transition probability function:

January 18, 2015 28University of Delhi

Step 3: Update Process

This process performs two operations to update the pheromone matrix.

.


Step 4: Decision ProcessCompare the final pheromone matrix (N) to

the threshold T .By comparing each pixel with the threshold to determine whether it is the edge or not

Divide the pheromone matrix (N) into two categoriesaccording to T(l): ML and MU


Take iterative index l = l+1 , update iteration threshold as:

If T (l ) T (n1) > , turn to the second step; otherwise, stop the iteration, and decide whether the (i, j) pixel is the image edge by using binary decision function. The judgment criterion is as follows:


2.4 ACO Flowchart


2.5 Experiment

Authors used SAR images having 5 ships with inherent speckle noise. After applying traditional edge detection algorithm, wavelet transform method and Ant Colony Optimization.


2.6 Result


a)Original image (b) Sobel detection result (c)Prewitt detection result

(d) The first iterative result of ACO (e) The second iterative result of ACO(f)WT detection result

(g) ACO detection result

2.7 Conclusion Ant Colony Optimization algorithm is adopted to adjust

the threshold dynamically.

Comparing with traditional edge detection operators and classical WT, ACO shows great superiority in dealing with image edge detection.

It needs multiple iteration to remove all speckle noise which sometimes may lead to loss in detail preserving feature.

ACO also behave some defects, such as: large searching time, more time wasted for the structure of the solution.


3. A Multi-objective Optimization Method Based on MOEA/D and Fuzzy Clustering for Change Detection in SAR Images.(Wang, Li, Gong, Su, Jiao-IEEE CEC14)


3.1 Introduction In this paper, change detection in SAR images integrates

evolutionary computation into fuzzy clustering process, for detail preserving and noise removal as two separate objectives for MOO.

Change detection is based on the comparative analysis of two images acquired from the same area at different times, with the purpose of detecting the change region between them.

It has been widely used in various fields, such as medical diagnosis, remote sensing, and video surveillance

This methods can be divided into two types:

i) Image threshold methods and

ii) Image classification methods.


3.2 Methodology A. Generation of Difference Images:

I1 & I2 are two image taken at t1 & t2 time of same geographical region.

Then average filter is applied to remove noise: Where Xi and Xi are the gray values of the i

th pixel of image In and Il , respectively, N represents a set of neighbours falling into a window of fixed size, S stands for the total number of set Nr


B. Two Objective Functions Selected for Multi-objective Optimization.

First FCM clustering is done to preserve details, so first function is:

uki is the fuzzy membership degree of the ith pixel with respect to cluster k. vk is value of cluster k.

For noise removing, the same cluster analysis is exerted on the filtered image In with most of the image noise removed, so function is:

The two objective functions can be combined into a multi-objective optimization problem.


3.3 Experiment Authors select the Bern, Ottawa and Yellow River Estuary

datasets as our change detection images.

Bern dataset before processing:


Fig. Images for Bern dataset : (a) Image acquired before flooding in April1999. (b) Image acquired after flooding in May 1999. (c) Image of the groundtruth.

(a) (b) (c)

3.4 Results


Fig. Six different change detect ion maps selected randomly from all theresults of the Bern dataset.

(a) (b) (c)

(d) (e) (f)

Results


5.5 Conclusion It converts the change detection problem into a multi-

objective optimization problem (MOP) by considering the image detail preserving and noise removing as two separate objectives.

A set of Pareto optimal solutions corresponding to all the decomposed sub-problems.

Noise Reduction is less compared to other MO algorithms.

It can be improved by updating the membership degree matrix to increase the effect.


Other MOO approaches: SAR image segmentation based on quantum-inspired

multi-objective evolutionary clustering algorithm (Li, Feng, Zhang, Jiao- IPL14) :

QMEC is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic

individuals.

Due to a set of non-dominated solutions in multi-objective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is

selected.


Review Studying all the research works, we can say that MOO

algorithm can be applied to decrease the speckle noise to enhance the segmentation quality. PSOGE is better only in sense of noise removal and MOEA & FCM is better in segment out the difference and ACO also did a good segmentation and helps in finding the edge of the object but it is time consuming.

From above all the merits and demerits, we can say that wehave to minimize the computing time as well as do the noise reduction.


Conclusion

From the survey we have come to an conclusion that Multi-objective optimization techniques are very useful in segmentation of SAR image rather than clustering algorithm.

We will apply the parallel computing approach in the MOO techniques in the very near future and will study the difference.


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3) Ma, Miao, et al. "A fast SAR image segmentation algorithm based on particle swarm optimization and grey entropy." Natural Computation, 2008. ICNC'08. Fourth International Conference on. Vol. 4. IEEE, 2008.

4) Wang, Qiao, et al. "A multiobjective optimization method based on MOEA/D and fuzzy clustering for change detection in SAR images." Evolutionary Computation (CEC), 2014 IEEE Congress on. IEEE, 2014.

5) Ma, Miao, et al. "SAR image segmentation based on Artificial Bee Colony algorithm." Applied Soft Computing 11.8 (2011): 5205-5214.


6) Li, Yangyang, et al. "SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm." Information Processing Letters 114.6 (2014): 287-293.

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Sar Image Segmentation Using Multi-objective Optimisation Technique

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