A SAR Image Segmentation Method Based on Adaptive Window Directional Wave Domain and Improved FCM

A technology of directional wave domain and image segmentation, which is applied in the field of image processing, can solve problems such as the limitations of segmentation methods and the severe influence of coherent speckle noise

Active Publication Date: 2017-02-15
XIDIAN UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the segmentation method existing in the existing method cannot fully consider the texture features and gray features of different regions in the SAR image,

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  • A SAR Image Segmentation Method Based on Adaptive Window Directional Wave Domain and Improved FCM
  • A SAR Image Segmentation Method Based on Adaptive Window Directional Wave Domain and Improved FCM
  • A SAR Image Segmentation Method Based on Adaptive Window Directional Wave Domain and Improved FCM

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Embodiment Construction

[0064] refer to figure 1 , the implementation steps of the present invention are as follows:

[0065] Step 1: Read the image and perform edge expansion on the image with a value of 2×n (n is of size 4, 8, 16, etc.).

[0066] Step 2: Move the window for each pixel of the SAR image, the size of the window is 2n×2n, and set the neighborhood windows in four directions for the current pixel f(i,j) to obtain the histogram data of the four window image blocks, The image patch similarity is calculated for every two histogram data.

[0067] (2a) According to step (1), perform edge expansion with a value of 2×n on the image, and set four windows with a size of 2n×2n for the current pixel f(i,j), namely (i-n+1:i+ n,j-2×n+1:j), (i-2×n+1:i,j-n+1:j+n), (i-n+1:i+n,j:j+ 2×n-1), (i:i+2×n-1,j-n+1:j+n) four windows;

[0068] (2b) Generate respective histogram data according to the pixel data of the two image blocks, and use the Bhattacharyya coefficient algorithm to calculate the similarity ...

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Abstract

The invention discloses an SAR image segmentation method based on a self-adaptive window directionlet domain and improved FCM. The problem that in an existing method, area segmentation is poor, and noise influence exists is mainly solved. The method comprises the steps that (1) each pixel of an SAR image is provided with a direction window, and histogram similarity measurement is carried out every two direction windows; (2) a threshold value T is set, when similarity is larger than T, two-layer Directionlet transformation is carried out on the 2n*2n windows with the pixels as centers, and when similarity is smaller than T, transformation is carried out on the n*n windows; (3) feature extraction is carried out on low-frequency coefficients and high-frequency coefficients after current block transformation, and the low-frequency coefficients and high-frequency coefficients are adopted as feature vectors of the pixels; (4), the step (1), the step (2) and the step (3) are circulated until whole image computation is completed; (5), the feature vectors are clustered through a fuzzy C-means algorithm with plesiomorphism distance improved; (6) corresponding gray level values are assigned to classified categories, and the final segmentation result is obtained. Richer textural features of the original image can be extracted, and correlation in data can be fully mined.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a SAR image segmentation method, which can be used for target recognition of SAR images and subsequent computer processing, in particular to a SAR image segmentation method based on adaptive window direction wave domain and improved FCM. Background technique [0002] Image segmentation is an important image technology and a major problem in low-level vision in the field of computer vision. It is a key step from image processing to image analysis and occupies an important position in image engineering. Image segmentation is also the basis for further image understanding. Image segmentation has been widely used in practice, involving almost all fields of image processing and applied to various types of images. For example, in medical applications, brain magnetic resonance (MR) images are segmented into gray matter, white matter, brain tissue such as cerebrospinal cord and other...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/11G06T2207/10044
Inventor 白静焦李成于文倩王爽马文萍马晶晶侯彪杨淑媛
Owner XIDIAN UNIV
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