Remote sensing image change detection method based on image fusion

Abstract

The invention discloses a synthetic aperture radar (SAR) image change detection method based on image fusion and mainly solves the problems that in prior art, detail information is lost and detection accuracy is low. The method includes extracting a differential difference chart and a specific value difference chart of an image before and after the change and extracting a variance matrix of a difference image; utilizing a KI threshold segmentation method to obtain an optimal threshold T; fusing the differential chart and the specific value chart according to the optimal threshold T and the variance matrix to obtain a final fused difference chart D; and subjecting a gray value of the obtained final difference chart D to segmentation of fuzzy C-means clustering with two categories, in a clustering center, one clustering with a larger value is used as a variable clustering, and the other clustering is used as a non-variable clustering, and the change monitoring result is obtained. According to the method, the defects that the differential chart is sensitive to noises and the specific value chart edge information is prone to lose are overcome, the error rate is reduced, and the detail information is excellently saved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to remote sensing image change detection, that is, detecting changes on the ground surface according to SAR images of different time phases, and can be used in the fields of ground object coverage and utilization, natural disaster monitoring, urban planning, map update and the like. Background technique [0002] The change detection of remote sensing images refers to detecting the change information of the ground features in the area over time by analyzing two or more remote sensing images from the same area at different times. Change detection in remote sensing images has been widely used in dynamic monitoring of forest resources, monitoring of changes in land cover and utilization, urban planning layout, environmental monitoring analysis, natural disaster assessment, and man-made target monitoring and ground armed deployment analysis in the military. and many other fields. ...

AI Technical Summary

Problems solved by technology

Among them, the image difference method and the ratio method are the most common difference map extraction operators. The image difference method is very sensitive to the change area of ​​the SAR image, which is conducive to retaining the geometric edge of the change information, thereby helping to accurately extract the change area. However, due to the influence of coherent speckle noise in SAR images, the difference method is not suitable for analyzing SAR images that are greatly affected by noise.

Although the image logarithmic ratio can effectively suppress the coherent speckle noise of the SAR image, it will cause certain blurring to the edge of the change area, especially in images with a large number of weak changes and small area change information, which is not conducive to the outline of the change area. and maintenance of details

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