Non-supervision change detection method based on fuse change detection operators and dimension driving

Abstract

The invention discloses a non-supervision change detection method based on fuse change detection operators and dimension driving. The non-supervision change detection method mainly solves the problems that due to the fact that image noise is eliminated through filter in the prior art, a large number of image details are lost, and the change detection difficulty is increased. The non-supervision change detection method is achieved through the steps of firstly, generating a logarithm ratio image and a mean value ratio image through image data for change detection; secondly, fusing the logarithm ratio image and the mean value ratio image to generate a primary difference image; thirdly, judging the reliable dimension of each pixel point on the primary difference image, and fusing the reliable dimensions of all the pixel points on the primary difference image to generate a change detection difference image; fourthly, conducting clustering on the change detection difference image to generate a change detection result image. More image details are reserved while noise is effectively restrained, the accuracy of the change detection result is improved, and the non-supervision change detection method can be used for change detection of an SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an unsupervised transformation detection method, which can be used for change detection of SAR images. Background technique [0002] Change detection belongs to the field of image processing, which refers to the quantitative analysis and determination of the characteristics and process of surface changes from remote sensing data of different periods. It usually deals with the problem of two-time phase change detection, and its essence is the change of the spectral response of image pixels in two periods caused by the change of surface characteristics with time. Modern remote sensing technology is developing rapidly, and microwave remote sensing has a certain penetration ability to ground objects due to its all-weather and all-weather working ability, and adopts side-view imaging, covering a large area. Compared with visible light and infrared remote sensing, Incomparabl...

AI Technical Summary

Problems solved by technology

[0004] The above is the commonly used SAR image unsupervised change detection method, which has the following disadvantages: 1. Although the filtering process in the preprocessing process can effectively eliminate the influence of multiplicative noise, it will inevitably cause the loss of image detail information, which is not conducive to subsequent image processing. The extraction of information is especially bad for some two-temporal image processing with huge differences in noise influence

2. Due to the complexity of SAR images, the difference images obtained by single operator processing usually have certain limitations

3. Directly analyze the image generated by the difference map operator, and the final change detection effect often depends on the selected difference map analysis method, and the results caused by different analysis methods vary greatly

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