Level set SAR image segmentation method based on local and global area information

Abstract

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method based on local and global area information, which mainly solves the problem that the existing level set method is influenced by speckle and cannot segment the SAR images with uneven gray. The method comprises the following implementation steps of: firstly, initializing a level set function phi, and segmenting the SAR image into an internal area omega 1 and an external area omega 2; secondly, convolving intensity information of the internal area and the external area of the image through a Gaussian Kernel Function, taking the convolved information as local area information, and forming an energy term based on the local area; then, solving intensity mean values c1 and c2 and probability densities p1 and p2 of the internal area and the external area, and forming the energy term of the global area; and finally, adding a bound term L (phi) of level set length and a penalty P (phi) which avoids renewed initialization, forming a total energy function ESAR, solving a gradient sinking equation through a variation method, and updating the level set phi. The obtained new segmented area and the experimental result show that the segmentation method provided by the invention can get more ideal segmentation effects, and the method can be used for SAR image segmentation and target identification.

Description

technical field [0001] The invention belongs to the field of image processing technology, and relates to the application of the field of SAR image segmentation, specifically a level set SAR image segmentation method based on the combination of local and global area information, which can be used for SAR image segmentation, edge detection and target recognition . Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution active radar with the advantages of all-weather, all-time, high resolution, and side-view imaging. It can be used in many fields such as military, agriculture, navigation, and geographical surveillance. 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. Since SAR is a coherent imaging system, the SAR image is essentially a response to the electromagnetic scattering characteristics...

AI Technical Summary

Problems solved by technology

However, it also has shortcomings. The internal energy term of the model only ensures the smoothness of the zero level set curve, without considering the intrinsic properties of the level set function itself. In some applications, the level set function needs to be reinitialized. , so that it is close to the signed distance function to ensure the stability of the numerical solution.

[0006] Although the level set segmentation method has achieved great success in optical and medical image segmentation, there is still relatively little research in the field of SAR image segmentation. At present, scholars who study the level set method based on regional information in the world include Chunming Li and Ayed et al., Chunming Li focused on the application of local binary fitting LBF method in medical image segmentation. Since SAR image noise is multiplicative noise, which is different from additive noise in medical images and natural images, local area methods are vulnerable to multiplicative Coherent speckle noise, so the LBF method is not suitable for SAR images

The level set method studied by Ayed et al. is prone to missing segmentation for more complex SAR images, and the segmentation results for SAR images in complex areas are not very ideal

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