Local Gamma fitting-based active contour SAR image segmentation method

Abstract

The invention discloses a local Gamma fitting-based active contour synthetic aperture radar (SAR) image segmentation method. The SAR image segmentation precision is low in the prior art. The method comprises the following steps of: representing the local structure of each pixel in an original SAR image in a certain neighborhood, regarding a maximum likelihood as a regional separation criterion, and defining a local fitting energy term; then integrating all pixel points in the defined region to acquire an overall optimized energy functional; and finally, describing and solving an energy minimization process by adopting a curve evolution theory and level set method-based geometrical active contour model so as to realize effective segmentation of the SAR image. The method has strong speckle noise inhibiting capability, can realize effective segmentation of the SAR image, and particularly has accurate segmentation effect on complex boundaries such as deep indentation and the like.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an image segmentation method, in particular to an active contour image segmentation method of a SAR image. Background technique [0002] Synthetic Aperture Radar (SAR) has become a widely used earth observation technology in military and civilian fields due to its all-day and all-weather high-resolution data acquisition capability. Image segmentation is the basic work of automatic interpretation of SAR images, which has received extensive attention. Since the SAR system adopts coherent imaging processing, there is a large amount of speckle noise in the SAR image, which is also an important obstacle to the accurate segmentation of the SAR image. In order to eliminate the influence of speckle noise, traditional SAR segmentation methods require coherent speckle preprocessing, but since preprocessing suppresses coherent speckles, it inevitably loses details such as potential boundaries,...

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Problems solved by technology

[0004] However, the existing ACM segmentation methods for SAR images are all based on the intensity statistics of the target objects in the image area, and their segmentation accuracy depends on the overall fitting degree of the statistical distribution to the image data, while ignoring the effective representation of the local structure of the boundary, resulting in The segmentation results are not ideal, especially when dealing with complex boundary conditions such as deep depressions.

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