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Automatic SAR image segmentation method based on graph division particle swarm optimization

A particle swarm optimization and automatic segmentation technology, applied in the field of image processing, can solve problems such as speckle noise sensitivity, unsatisfactory segmentation results, and difficulty in image interpretation, achieving strong robustness, reducing complexity, and optimizing the number of convergence categories. Effect

Active Publication Date: 2017-09-29
XIDIAN UNIV
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Problems solved by technology

Synthetic aperture radar has the characteristics of high resolution, all weather, and strong transmission. It is relatively easy to acquire SAR images, but it is difficult to interpret them. SAR image segmentation is a key technology for image interpretation. The development of remote sensing is of great significance
[0003] In recent years, the image segmentation method based on the particle swarm optimization algorithm has been applied to the segmentation of SAR images, including artificial immune system, particle swarm optimization and multi-agent and other evolutionary paradigms, but because this method is very sensitive to the speckle noise contained in SAR images Sensitive, so the segmentation results are not ideal

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  • Automatic SAR image segmentation method based on graph division particle swarm optimization
  • Automatic SAR image segmentation method based on graph division particle swarm optimization
  • Automatic SAR image segmentation method based on graph division particle swarm optimization

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[0032] Implementation and effect of the present invention are described in further detail below in conjunction with accompanying drawing:

[0033] refer to figure 1 , the implementation steps of the present invention are as follows:

[0034] Step 1. Input the original image I to be segmented, and read the gray gradient information of the image.

[0035] Step 2. Select the optimal value according to the experiment, including the neighborhood window radius d s , search window radius D s and Gaussian smoothing parameter h, and perform non-local mean filtering to denoise the image I to be segmented to obtain a gradient image.

[0036] The specific implementation of this step is as follows:

[0037] 2a) In this example, the neighborhood window radius is set but not limited to d s =2, search window radius D s = 5;

[0038] 2b) The smoothing parameter of the Gaussian function in this example is taken but not limited to h=10, which controls the attenuation degree of the exponen...

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Abstract

The invention discloses an automatic SAR image segmentation method based on graph division particle swarm optimization and mainly solves a problem of poor image segmentation effect in the prior art. The method comprises steps that 1, an original to-be-segmented image is inputted, and the gray information is read; 2, the to-be-segmented image is filtered to acquire a gradient image; 3, the gradient image is divided into non-overlapped regions; 4, the largest class quantity of the gradient image is solved and is taken as the largest image gray level; 5, the segmented regions are mapped to be undirected weighted graphs, and an energy function of the undirected weighted graphs is constructed; 6, iteration solution of the energy function is carried out to acquire a class center and the class quantity; and 7, whether iteration frequency is smaller than 20 is determined, if yes, particle update continues, if not, the optimal class quantity and the images after segmentation are outputted. The method is advantaged in that the operation speed is fast, the segmentation effect is good, and the method can be applied to medical images, satellite image positioning, face identification, fingerprint identification, traffic control systems and machine vision.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a SAR image automatic segmentation method, which can be used in medical imaging, satellite image positioning, face recognition, fingerprint recognition and traffic control systems. Background technique [0002] With the continuous advancement of science and technology, image processing technology is more and more widely used in our production and life, and as an important branch of image processing field, image segmentation technology is also more and more valued by people. Image segmentation is a key step in the process of image interpretation, and image segmentation technology refers to the technology of extracting meaningful feature parts in images. Common applications such as: medical imaging, satellite image positioning, face recognition, fingerprint recognition, traffic control systems, machine vision, etc. are all examples of the application of segment...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/12G06T7/136
CPCG06T7/12G06T7/136G06T2207/10044G06T2207/20081G06T2207/20152
Inventor 刘若辰焦李成卢成林夏冠张丹李阳阳刘静王爽
Owner XIDIAN UNIV
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