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Fuzzy cluster SAR image segmentation method based on Gamma distribution

An image segmentation and fuzzy clustering technology, applied in the field of image processing, can solve problems such as the inability to solve the SAR image segmentation problem, and achieve the effects of improving fitting and segmentation accuracy, fast convergence speed, and good stability.

Inactive Publication Date: 2016-07-20
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, this method believes that the distribution of SAR images is Gaussian distribution, which is inconsistent with the characteristics of SAR images obeying Gamma distribution, and cannot solve the segmentation problem of all SAR images.

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  • Fuzzy cluster SAR image segmentation method based on Gamma distribution
  • Fuzzy cluster SAR image segmentation method based on Gamma distribution
  • Fuzzy cluster SAR image segmentation method based on Gamma distribution

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Embodiment Construction

[0039] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0040] A fuzzy clustering SAR image segmentation method based on Gamma distribution, such as figure 1 shown, including the following steps:

[0041] Step 1: read the SAR image to be segmented;

[0042] In this embodiment, the SAR image domain to be segmented is defined as X={x 1 ,...,x i ,...,x N}, where x i is the grayscale measure of the i-th pixel, i is the pixel index, N is the total number of pixels, X is 128×128 pixels, and the total number of pixels is N=16384.

[0043] Step 2: The gray value of each pixel of the SAR image to be segmented is used as a sample point to construct an FCM objective function with a Gamma distribution function. The objective function aims to overcome the influence of noise in the SAR image on the SAR image segmentation result. The gray distribution obeys the Gamma distribution, and the neg...

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Abstract

The invention provides a fuzzy cluster SAR image segmentation method based on Gamma distribution. The method comprises the following steps: taking ray values of pixels of an SAR image to be segmented as sampling points, and constructing an FCM object function with a Gamma distribution function; determining solution formulas of parameters in the FCM object function; carrying out fuzzy clustering on the SAR image to be segmented by use of the FCM object function with the Gamma distribution function to obtain fuzzy membership degree matrixes when the gray value of each pixel of the SAR image to be segmented belongs to each ground object type; and performing defuzzification on the fuzzy membership degree matrixes according to a maximum membership degree principle so as to realize SAR image segmentation. According to the invention, a dissimilarity measure from pixel points to clusters is described by use of negative logarithms of a Gamma distribution probability density function, through accurate fitting of SAR image distribution features, the influence exerted by noise in the SAR image on a segmentation result is further overcome, the segmentation precision is improved, and the fitting and segmentation precision of the SAR image is effectively improved.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a Gamma distribution-based fuzzy clustering SAR image segmentation method. Background technique [0002] Synthetic Aperture Radar (SAR) image segmentation is a key technology in SAR image processing, and its segmentation accuracy directly affects the subsequent interpretation processing. Therefore, the research on SAR image segmentation technology is of great significance. However, it is difficult to obtain high-precision segmentation results due to the fact that the imaging principle of SAR is contrary to human vision, the high resolution brings clearer details, and the inherent speckle noise caused by the imaging process. [0003] At present, SAR-based image segmentation methods can be mainly divided into: threshold method, statistical method and clustering method. The threshold-based method is simple in principle, but it is difficult to find a reasonable threshold ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T2207/10044G06T2207/30181G06F18/2321
Inventor 王春艳徐爱功杨本臣姜勇
Owner LIAONING TECHNICAL UNIVERSITY
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