Remote sensing image registration method based on multiple feature points

Abstract

The invention discloses a remote sensing image registration method based on multiple feature points, and mainly solves the disadvantages of low accuracy of conventional multisource and multispectral remote sensing image registration. The realization steps are that 1. two remote sensing images are inputted; 2. the anisotropic scale space of the input images is constructed; 3. Harris and Hessian operators are respectively used in the anisotropic scale space of the input images to perform feature point detection; 4. the two feature point detection results of the input images are combined so as to generate feature vectors; and 5. the feature vectors of the input images are matched, the mistakenly matched feature point pairs are deleted and registration results are outputted. The remote sensing image registration method based on the multiple feature points has advantages of high registration accuracy and can be used for registration of multisource remote sensing images and multispectral remote sensing images.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to registration of remote sensing images, and can be applied to registration of multi-source and multi-spectrum remote sensing images. Background technique [0002] Image registration is a very critical step in image processing. It refers to the process of superimposing and aligning two or more images of the same scene acquired at different times, from different perspectives, or from different sensors. Image registration technology has been widely used in image change detection, image mosaic, medical field and pattern recognition field. Image registration methods can be roughly divided into two categories: image registration methods based on gray information and image registration methods based on feature information. [0003] Image registration based on grayscale is based on the grayscale information between the pixels of the entire image, and establishes a similarity measureme...

AI Technical Summary

Problems solved by technology

The classic feature point extraction operators include: Harris operator, Hessian operator, etc., but the Harris operator is mainly for corner feature detection, and the poor detection effect for images with many texture features leads to inaccurate image registration. The Hessian operator It is mainly aimed at texture feature detection, and the registration results of images with more corner information are inaccurate

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