Immune clone quantum clustering-based SAR image segmenting method

Abstract

The invention discloses an immune clone quantum clustering-based SAR image segmenting method, which relates to the technical field of image processing, and mainly solves the problem of limitation on the application of the conventional quantum clustering technology in a large-scale data set. The immune clone quantum clustering-based SAR image segmenting method is implemented by the following steps: 1) extracting features of an SAR image to be segmented; 2) initializing an antibody population and coding antibodies; 3) calculating antibody affinity according to quantum mechanical characteristics, and dividing the antibody population into an elite population and a general population; 4) designing different immune clone optimization operators for the elite population and the general population respectively, and performing a cloning operation, a normal cloud model-based adaptive mutation operation, a uniform hypermutation operation, a clonal selection operation and a hypercube interlace operation orderly; and 5) outputting an SAR image segmentation result. The immune clone quantum clustering-based SAR image segmenting method has high iteration optimization speed and high stability, can effectively segment the SAR image which contains large-scale data volume, and is suitable for object detection and identification of the SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to SAR image segmentation, and can be used for radar target detection and target recognition. Background technique [0002] Clustering refers to the use of mathematical methods to study and deal with the classification of specific objects without any prior knowledge about the samples, and to divide a sample without a category mark into several subsets according to certain criteria, so that similar samples can be classified as much as possible. may be classified into one category, while dissimilar samples are divided into different categories as much as possible. Cluster analysis is a kind of multivariate statistical analysis and an important branch of unsupervised pattern recognition. Existing clustering algorithms can be roughly divided into partition-based clustering, hierarchical-based clustering, density-based clustering, grid-based clustering, model-based clustering, and fu...

AI Technical Summary

Problems solved by technology

However, quantum clustering is easy to fall into local extremum when iterating through the gradient descent method. At the same time, the slow iteration speed limits its application in large-scale data sets, especially in the field of image segmentation.

Although there are some improved technologies, such as improvements to distance measures, improvements to scale parameter estimation, etc., none of them can fundamentally solve the above bottleneck problems

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