Heterologous image accurate matching method

Abstract

The invention discloses a heterologous image accurate matching method, which comprises the following steps: firstly, respectively matching an infrared grayscale image and a negative image thereof witha visible light grayscale image, namely two to-be-matched image groups; secondly, for each image of the to-be-matched image group, extracting key points with invariable scales, and calculating LPQ feature vectors of neighborhoods of the key points; thirdly, performing weighted fusion on the SIFT feature, the shape context feature based on the angular point and the LPQ feature of each image in theto-be-matched image group, and performing initial matching on the images through a nearest neighbor ratio method; fourthly, removing mismatching points; and finally, integrating the matching resultsof the infrared grayscale image and the negative image thereof with the visible light grayscale image into a final matching result. According to the method, the context descriptor is designed to obtain global information of the image, meanwhile, in order to further improve the matching performance, the LPQ descriptor is utilized to obtain image fuzzy invariant texture features, and finally, the method can obtain a heterologous image accurate matching result.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for precise matching of heterogeneous images. Background technique [0002] The matching technology of heterogeneous visual images (hereinafter referred to as heterogeneous images) is a key technology widely used in visual navigation, pattern recognition, guidance and terrain measurement of aircraft, remote sensing satellites, missiles, etc. These systems often use different imaging mechanisms sensor. The so-called heterogeneous image matching is a technique for matching images from different imaging sensors. These images are images of the same scene or object or target formed by different imaging sensors under different imaging times and lighting environments, mainly including visible light images, infrared images, and radar aperture images (SAR images). Due to the differences in the structure and imaging principle of different types of sensors, there are lar...

AI Technical Summary

Problems solved by technology

However, at present, many literatures at home and abroad introduce heterogeneous image matching algorithms based on a single feature. Because a single feature does not

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