SAR image segmentation method based on manifold distance two-stage clustering algorithm

Abstract

The invention discloses an SAR image segmentation method based on a manifold distance two-stage clustering algorithm. The method mainly solves the problem that an existing clustering segmentation algorithm is unstable in result and big in calculated amount. Achieving steps includes: (1) setting an ending condition and an operation parameter; (2) inputting an image to be segmented, and conducting pre-segmentation on the image to be segmented; (3) extracting characteristics of an image block obtained by pre-segmentation; (4) taking Euclidean distance to be used as similarity measurement to conduct first stage clustering for the characteristics of the image block; (5) taking a clustering center of a first stage and a point farthest from the center as representative points; (6) calculating a manifold distance between any two representative points; (7) taking the manifold distance as the similarity measurement to conduct clustering of a second stage of the representative points; (8) refreshing a clustering center; and (9) judging whether an end condition is achieved, if the end condition is not achieved, returning the step (7), or outputting a segmentation result image. The method has the advantages of being accurate in segmentation result, stable, short in time, and capable of being used in the technical fields such as image strengthening, pattern recognition and target tracking.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation, and can be used in technical fields such as image enhancement, pattern recognition, and target tracking. Background technique [0002] As an important step in the image processing process, the main task of image segmentation is to divide the input image into some non-overlapping regions, so that the same region has the same attributes, and different regions have different attributes. For the image segmentation problem, researchers have proposed many methods, but in view of the characteristics of many types of images, large amount of data, and many changes, so far there is no method that can be applied to all images. As a method of image segmentation, data clustering has been widely used. [0003] As an important method of analyzing data, clustering has been widely concerned in many fields. It is a process of classifying data according to certain measuremen...

AI Technical Summary

Problems solved by technology

However, as a search algorithm, the evolutionary algorithm is easily disturbed by the local optimal solution and makes mistakes in the process of finding the global optimal solution.

[0006] Because the shortcomings of the above-mentioned traditional clustering algorithm have a great impact on the clustering performance, it limits the application of the clustering algorithm in image segmentation. top priority

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