Level set SAR (Synthetic Aperture Radar) image segmentation method based on self-adaptive finite element

Abstract

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method based on a self-adaptive finite element, which is mainly used for solving the problem that a conventional variational level set model based on statistical distribution is imprecise in the non-homogeneous SAR image segmentation. The method comprises the concrete implementation steps of: (1) optimizing an image partitioning energy term on the basis of minimum cutset criterion of image partitioning; (2) defining the weighted energy functional through combining with a level set rule term and a length bound term; (3) carrying out variation and minimization on the energy functional to obtain a curve evolution control equation; (4) carrying out discretization on a finite element mesh to obtain a semi-implicit discrete scheme of the curve evolution control equation; and (5) adjusting strategy by adopting the self-adaptive finite element mesh based on posteriori error estimate, realizing the level set evolution based on a triangular mesh and obtaining a segmentation result of the SAR image. According to the invention, the energy functional is defined by utilizing pairing similarity so that the limitation of the conventional statistical model is overcome; in the meantime, the numerical computation strategy based on the self-adaptive finite element is adopted so that the effective balance of segmentation quality and computing efficiency is realized.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image segmentation method, in particular to a level set segmentation method of a synthetic aperture radar (Synthetic Aperture Radar, SAR) image. technical background [0002] Compared with optical imaging systems, synthetic aperture radar (SAR) systems have become an indispensable earth observation technology in military, agricultural, urban planning and other applications due to their all-weather and all-weather data acquisition capabilities. With the continuous development of SAR equipment and imaging technology, the intelligent interpretation technology of SAR images is facing new challenges. As a key step in SAR image interpretation, image segmentation has attracted much attention. However, due to the coherent imaging principle of the SAR system, the SAR image is deeply affected by the coherent speckle noise, which becomes an important obstacle to the accurate segmentation of...

AI Technical Summary

Problems solved by technology

However, the statistical model is based on the assumption that the intensity of the target and the background area satisfy a certain probability distribution. The fitting accuracy of the statistical distribution to the image data determines the final image segmentation quality, so its application range is very limited.

In fact, due to design flaws based on prior assumptions, statistical models cannot fundamentally solve the problem of accurate segmentation of SAR images in complex scenes, especially high-resolution SAR images with heterogeneous features such as dark textures, strong scattering, and weak boundaries.

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