High-resolution remote sensing image feature matching method

Abstract

The invention discloses a high-resolution remote sensing image feature matching method. The method comprises the steps of extracting salient object regions on a reference image; extracting the SIFT feature points of the reference image and the SIFT feature points of an input image; searching for a plurality of candidate matching points for each SIFT feature point of the reference image from a SIFT feature point set of the input image; searching for optimal matching points for a SIFT feature point set in each salient object region of the reference image from a candidate matching point set composed of corresponding candidate matching points; obtaining an optimal matching point set of each salient object region, and using the union set of SIFT feature optimal matching point sets corresponding to each salient object region as a final feature matching set. According to the high-resolution remote sensing image feature matching method, matching is carried out based on the SIFT features in the salient object regions, a large amount of redundant information is filtered, and matching performance is improved; the number of exterior points is reduced, and robustness to change of view and image deformation is improved. The high-resolution remote sensing image feature matching method can be widely used in multiple application systems for image registration and three-dimensional reconstruction.

Description

technical field [0001] The invention relates to the technical fields of remote sensing image processing, feature matching, image registration, etc., in particular to a high-resolution remote sensing image feature matching method. Background technique [0002] Image registration is the process of geometrically matching two images (one as a reference image and the other as an input image) taken at the same location, at different times, or with different sensors. Image registration is the premise and foundation of applications such as image fusion and change detection. The accuracy of image registration has an important impact on subsequent applications. [0003] The current image registration algorithms can be divided into pixel-based registration methods and feature-based registration methods. Due to the influence of noise, shooting conditions, seasonal changes, viewing angle changes, platform shake and other factors, feature-based registration methods are more suitable for ...

AI Technical Summary

Problems solved by technology

Filter out vegetation, desert and other backgrounds from the SIFT feature set, and comprehensively utilize the appearance and geometric topology similarity between the SIFT feature sets of salient target areas is an important way to improve the matching accuracy, but there is no effective related algorithm yet.

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