Non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method

An image segmentation, non-local technology, applied in the field of target recognition, can solve problems such as insufficient utilization of image context information, loss of image detail information and edges, and consistency of SAR image mis-segmented areas

Inactive Publication Date: 2013-01-30
XIDIAN UNIV
View PDF2 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional TMF method does not consider the similarity of the image itself, so the context information of the image is not fully utilized, resulting in the loss of some image detail in

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method
  • Non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method
  • Non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

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

[0034] Step 1: Input the SAR image to be segmented, the size of the image is M*N, the number of views is n, and the image contains targets such as farmland, water area, forest, town, and mountain.

[0035] Step 2, using the FCM clustering method to obtain the initial class label of each pixel of the image to be segmented.

[0036] FCM clustering is performed on the SAR image to be segmented, and each pixel belongs to each category with different degrees of membership. After iterative optimization, each pixel is assigned to the class with the highest degree of membership, and the initial class label of each pixel in the image is obtained.

[0037] Step 3, extract the gray level co-occurrence matrix of the image to be segmented, use the k-means clustering method to obtain the scene category of each pixel of the image to be segmented, and use the non-local redundant information of the...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a non-local-based triple Markov random field synthetic aperture radar (SAR) image segmentation method and belongs to the technical field of image processing. The problems that a traditional triplet Markov field (TMF) method which is used in SAR image segmentation is poor in regional consistency and disorder in edge are solved. The method comprises steps of (1) inputting an image to be segmented; (2) initializing all pixel class marks by using fuzzy C-means (FCM) clustering; (3) initializing all pixel scene categories by using k-means and conducting iteration for scene categories by using non-local redundant information; (4) calculating potential energy of the image; (5) constructing triple Markov random field joint distribution, conducting function sampling for the distribution by using a Gibbs sampler and obtaining the posterior probability; (6) calculating the edge posterior probability and updating all pixel class marks gradually; and (7) determining whether the change rate of all pixel class marks is larger than the threshold, repeating step (4), step (5) and step (6) if the change rate of all pixel class marks is larger than the threshold, and inputting segmentation results if the change rate of all pixel class marks is not larger than the threshold. The method has the advantages of being quick in convergence velocity, good in segmentation result regional consistency, capable of retaining complete information and applied to SAR image target identification.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for segmenting non-stationary SAR images with uneven texture distribution, which can be applied to target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution radar system that can be used in many fields such as military affairs, agriculture, navigation, and geographical surveillance. It has many differences compared with other remote sensing imaging systems and optical imaging systems. In terms of military target recognition, SAR images are widely used in the field of target detection, and SAR image segmentation is an important step from image processing to image analysis, and is the basis of target classification and recognition. [0003] In terms of SAR image segmentation processing, due to the existence of inherent multiplicative speckle noise in SAR images, the pixels of the image often have mutations, which are only locally i...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00
Inventor 侯彪焦李成牛佳颖马文萍张向荣王爽
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap