SAR image change detection method based on artificial immune multi-objective clustering

Abstract

The invention discloses an SAR image varying detecting method based on artificial immunity multi-target clustering, and mainly solves the problems of low accuracy and low efficiency of an SAR image varying detection result. The realizing steps of the method comprise: (1) reading-in two time-phase SAR images; (2) constructing difference images of the two time-phase SAR images; (3) performing gray value-based self-adaptive immunity multi-target clustering on the difference images, and dividing the images into a varying type, a non-varying type and a to-be-identified type; (4) carrying out non-subsample wavelet conversion-based immunity clone multi-target clustering on the to-be-identified type to obtain a group of clustering center of the to-be-identified type; (5) performing minimum distance classifying on the to-be-identified type according to the group of clustering center to obtain one group of varying detection outcome images; (6) calculating the target function values of the varying detection outcome images; (7) selecting the minimum target function value according to the target function value; and (8) taking the varying detection result corresponding to the minimum target function value as the final detection result. The SAR image varying detecting method based on the artificial immunity multi-target clustering has the advantages of high detection efficiency and high detection precision.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for image change detection, in particular to a method for detecting SAR image changes in the same area at different times, which can be used to extract multi-temporal SAR images and obtain information on ground object change characteristics and change processes. Background technique [0002] With the development of image processing technology, the change detection of optical remote sensing images has been well developed at present, but the optical remote sensing images are seriously affected by bad weather, and can not get better ground object information, while synthetic aperture radar SAR has full Weather, all-weather, large coverage area and other characteristics, so the change detection of SAR images has a wider application prospect. In recent years, with the vigorous development of SAR image change detection research technology at home and abroad, many novel and effect...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: first, initializing the population with all possible solutions leads to low initialization efficiency and reduces the convergence speed of the algorithm; second, the solution of a single objective function causes the obtained solution to be biased towards a certain target, which cannot be fully measured The comprehensive performance of the solution makes the obtained solution easy to fall into local optimum, thus reducing the accuracy of change detection

The shortcomings of this method are: first, the solution of a single objective function causes the solution to be biased in a certain direction, and cannot comprehensively measure the comprehensive performance of the problem; The degree information does not use the texture and area information of the difference map, which increases the pseudo-change information; the third is to analyze the difference map as a whole during the solution process, which will easily cause the result to fall into a local optimal solution, which will reduce the accuracy of change detection. Spend

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