Remote sensing image registration method based on local configuration covariance matrix

Abstract

The invention provides a method for registering a remote sensing image based on a local contour covariance matrix, which combines a corner feature, a local split image and a local edge contour together as a local feature to implement an extraction of control points, has the local contour covariance matrix introduced into a registering processing, uses the local contour covariance matrix to implement an automatic extraction of a rotating and scaling factor, and combines a technology of self adaptive selection of a local window to reduce an effect of the twiddle and scaling factor on the registering processing so as to improve the robustness of the registering processing, thereby implementing an accurate registering between remote sensing images in a mode of large rotation and scaling without human participation.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image registration method, in particular to a remote sensing image registration processing method based on a local contour covariance matrix. Background technique [0002] Image registration is a basic problem in image processing, which is the process of matching and superimposing two or more images of the same scene taken from different times, different sensors or different perspectives. To be precise, the goal of image registration is to find the best mapping relationship between input images. [0003] At present, image registration processing methods can be mainly divided into three categories: [0004] 1. Registration processing method based on global grayscale statistical information; [0005] The registration processing method based on the global gray level statistical information directly uses the gray level statistical properties of the image itself to measure the simil...

AI Technical Summary

Problems solved by technology

[0011] In terms of feature matching relationship establishment, the traditional method is to measure whether two points in the image are consistent by statistical parameters such as local correlation coefficient or mutual information, but this method is greatly affected by the rotation factor and scaling factor, with the scaling factor and As the twiddle factor increases, its correlation decreases rapidly

Similarity testing using affine invariants relies on extracting fully closed edge profiles, which is often difficult to achieve for remote sensing images, especially radar images

Using the aggregation of point features to achieve similarity matching requires that the point features extracted from each input image have a high consistency, which is also difficult to achieve for remote sensing images

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