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

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...

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 information and edges in the segmentation process, resulting in the TMF method for SAR images containing complex textures. Mis-segmentation and unsatisfactory regional consistency will occur

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