Feature-matching method based on keypoint response constraint using binary encoding of phase congruency

At present, the cross-view geo-localization (CGL) task is still far from practical. This is mainly because of the intensity differences between the two images from different sensors. In this study, we propose a learning feature-matching framework with binary encoding of phase congruency to solve the problem of intensity differences between the two images. First, the autoencoder-weighted fusion method is used to obtain an intensity alignment image that would make the two images from different sensors comparable. Second, the keypoint responses of the two images are calculated using the binary encoding of the phase congruency theory, which is employed to construct the feature-matching method. This method considers the invariance of the phase information in weak-texture images and uses the phase information to compute the keypoint response with higher distinguishability and matchability. Finally, using the two intensity-aligned images, a method for computing the binary encoding of the phase congruency keypoint response loss function is employed to optimize the keypoint detector and feature descriptor and obtain the corresponding keypoint set of the two images. The experimental results show that the improved feature matching is superior to existing methods and solves the problem of view differences in object matching. The code can be found at https://github.com/lqq-dot/FMPCKR.