Objective

2

Synthetic aperture radar (SAR) systems are widely applied in many areas

such as civil and mili-tary fields

because they can operate day and night under various weather conditions. As a key and basic section of target recognition and interpretation for SAR images

SAR image segmentation has received much attention in recent years. However

SAR images suffer from strong speckle noise due to the influence of coherent illumination

which makes target segmentation in images difficult. Considering the importance of SAR image segmentation

this study proposes to segment the targets of a SAR image automatically. Many algorithms can solve SAR image target segmentation

and one of these is the GrabCut algorithm. The GrabCut algorithm

which is based on graph theory

achieves optimal segmentation and transforms the image segmentation problem into a problem of computing the maximum flow in the flow network. After this transformation

the problem can be solved with the min-cut or max-flow method. Nevertheless

the GrabCut algorithm has crucial deficiencies; for example

it not only requires artificial interaction but also merely utilizes one of the following:information

texture

or boundary information in the images. To improve such deficiencies

this study uses two kinds of information

namely

texture and boundary

for utilization in SAR images and for achieving automatic target segmentation.

Method

2

The proposed algorithm involves several steps. First

the proposed algorithm transforms a colored or gray SAR image into a 24-bit bitmap that contains substantial SAR image information. Second

with the aid of the 24-bit bitmap

a SAR image model is built according to graph theory. The model is a Gaussian mixed model that assigns each pixel in SAR images into three types of chroma spaces. Under the model framework

the energy function of the minimized description diagram is generated. Third

to segment the target region in the SAR image

the max-flow method is applied to determine the smallest cut set of the energy function in the description diagram. Coherent noise has a serious impact on image segmentation; thus

the proposed algorithm utilizes the median filter method to reduce noise in the target region and achieve precise SAR image target segmentation. Lastly

one of main problems in segmentation is that the interference of SAR image spots and small objects leads to incorrect targets during target segmentation. The neighborhood growth method

which removes specks in the SAR image target region and filters out small targets

is applied to tackle this problem and connect the target boundary. Through these steps

SAR image target segmentation can be performed automatically.

Result

2

Several state-of-the-start image segmentation algorithms

including mean-shift and Otsu segmentation algorithms

are compared with the proposed algorithm to validate its superiority. All experiments are performed in the processing platform of MATLAB R2014 in a 64-bit Windows 7 environment. In the first experiment

four different SAR images involving buildings

garages

trees

and cars are tested for image target segmentation. Results show that the proposed algorithm can segment many useful characteristics in the targets and can effectively remove background clutter

target shadows

and small interfering targets in the tested SAR images. This capability demonstrates that the proposed algorithm performs well in SAR image segmentation. A second experiment is conducted to illustrate the performance of the proposed algorithm. Here

mean-shift and Otsu segmentation algorithms and the proposed algorithm are simultaneously tested using four SAR images. As shown in the figures and tables

the proposed algorithm exhibits the best performance in SAR image target segmentation among all of the tested algorithms. The mean-shift algorithm can segment SAR image targets

but the contour boundary of the targets is fuzzy

and the computational efficiency is low. The Otsu segmentation algorithm can hardly segment targets correctly. Meanwhile

the proposed algorithm can split image targets accurately and reduce the computation time.

Conclusion

2

One of the most common methods of SAR image target segmentation is the GrabCut algorithm. However

GrabCut's precision of target segmentation is considerably affected by image background clutter and coherent noise. Meanwhile

targets in SAR images are often shielded

resulting in inaccurate image segmentation results. To address these problems

the proposed algorithm builds a Gaussian mixed model then transforms the target segmentation problem into a problem of minimizing the energy function of the description diagram. The max-flow method is used to determine the smallest cut set. The proposed algorithm can segment SAR image targets accurately through multiple iterations of the max-flow method in the SAR image color space domain and by using a median filter to remove specks in the SAR image target region and filter out small targets. However

the model in the proposed algorithm is not adaptive and thus cannot perform well in target segmentation of various SAR images. Moreover

the proposed algorithm applies the max-flow method to compute the max flow in the description diagram

thus spending considerable time in searching. In the future

we will further improve the precision of target segmentation by using an adaptive Gaussian mixed model and several useful approaches for computing the max flow to improve the performance of SAR image target segmentation.