Multi-target Fuzzy Clustering Image Change Detection Method Based on Non-Dominated Neighborhood Immune Algorithm

Abstract

The invention discloses a multi-target fuzzy cluster image variance detecting method based on a non-control-neighborhood immune algorithm, and solves the problem that details and noises cannot be balanced by a conventional cluster algorithm. The realization method comprises the following steps: setting the iteration number and other operation parameters; randomly generating an initial population based on a center code; taking the similarity measure of the Euclidean distance among pixels as well as the similarity measure of the Euclidean distance among spatial points and neighborhoods thereof as the optimization targets; updating membership grade; updating superiority antibody groups according to the optimization targets; selecting the non-control-neighborhood; immunizing the antibody group, and performing circulation when necessary; judging whether the end condition is met or not, obtaining the cluster result through the membership grade if the end condition is met, and outputting the divided images. The multi-target fuzzy cluster image variance detecting method introduces the multi-target method into the cluster algorithm containing spacial information to solve the problem that the details and the noises are difficult to balance during image dividing, and can be applied to the technical field of image dividing, target identification, and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to the application of a multi-objective evolutionary algorithm in image clustering and segmentation, and can be used in technical fields such as image change detection, image segmentation, image classification, pattern recognition, and target tracking. Background technique [0002] Remote sensing image change detection is to obtain the required ground object change information through the comparison and analysis of two or more remote sensing images in different periods of the same area, and the differences between the images. At present, the research methods of SAR image change detection algorithm are roughly divided into two types: the first is the post-classification comparison method, and the second is the difference map classification method. The difference map classification method is currently recognized as a more effective method, that is, to construct a difference ima...

AI Technical Summary

Problems solved by technology

However, both FCM_S algorithms add a parameter to balance the weight between noise and image details when introducing neighborhood space information, and the selection of parameters is often uncertain and needs to be obtained through repeated testing.

If the parameters are not selected properly, it will affect the segmentation effect of the algorithm.

The fuzzy local information C-means algorithm (FLICM) algorithm expects to construct a factor that does not contain parameters and can balance noise and image details, avoiding the difficulty of parameter selection, but in actual calculations, it cannot effectively converge, and according to The Grangian multiplier method to find the minimum value of the objective function does not give the results of the cluster center and membership matrix

[0005] On the other hand, the traditional clustering algorithm uses the method of randomly selecting the cluster center to initialize, which is likely to have a great impact on the accuracy of the clustering results.

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