Unmanned aerial vehicle aerial image accurate matching method based on island edge characteristics

Abstract

The invention provides an unmanned aerial vehicle aerial image accurate matching method based on island edge characteristics and relates to the field of computer graphics. The unmanned aerial vehicle aerial image accurate matching method solves the problem of dislocation easily occurring during splicing of unmanned aerial vehicle aerial images for island edges. Anisotropic Gaussian filters and weighted median filters in weight dynamic allocation are adopted to filter noises in island aerial images, a chordal distance fixed point self-adaption linear fitting method is utilized to detect edge angular points, a maximum likelihood estimation sample consensus (MLESAC) algorithm is utilized to screen matching points, the screened matching points are substituted into an affine transformation model to solve parameters, and matched modified functions are utilized to perform accurate registration on the images. According to the unmanned aerial vehicle aerial image accurate matching method, island edge details are effective reserved, the extracted island edges are clear, and the edge angular points can be extracted rapidly. A screening and correction process of the angular points is designed, the island images are matched accurately, and the matching effect is good.

Description

technical field [0001] The invention relates to the field of computer graphics, in particular to the extraction of image edge features, the recognition of image edge corner points and the precise registration between images. Background technique [0002] The edge is the dividing line of different regions, and it is a collection of pixels with the most significant local intensity changes in the image. As an essential link in feature extraction, edge detection is a basic problem in the field of image processing and computer vision. Image edge detection can not only greatly reduce the data volume of image processing but also preserve the important structural information of the image. Edge detection can be roughly divided into two types: search-based edge detection and zero-crossing-based edge detection. Edge detection based on search is mainly realized by solving the extremum in the first derivative of the image, and usually locates the boundary in the direction of the larges...

AI Technical Summary

Problems solved by technology

The filtering characteristics of conventional edge extraction operators cannot meet the requirements, which often results in unclear edge extraction of islands and large errors in matching point selection.

Moreover, the correction of matching points in the follow-up process is not perfect enough, resulting in misplaced or mismatched splicing of island edge graphics.

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