A Method for Detecting Feature-Sparse Regions and Matching Enhancement

Abstract

Image matching is a key research issue in the intelligent processing of remote sensing images. Due to the large phase differences or apparent differences in ground features between unmanned aerial vehicle imagery and satellite imagery, as well as the large number of sparsely textured areas, image matching between the two types of imagery is very difficult. Tackling the difficult problem of matching unmanned aerial vehicle imagery and satellite imagery, a feature sparse region detection and matching enhancement algorithm (SD-ME) is proposed in this study. First, the SuperGlue algorithm was used to initially match the two images, and feature-sparse region detection was performed with the help of the image features and initial matching results, with the detected feature sparse areas stored in a linked list one by one. Then, according to the order of storage, feature re-extraction was performed on the feature-sparse areas individually, and an adaptive threshold feature screening algorithm was proposed to filter and screen the re-extracted features. This retains only high-confidence features in the region and improves the reliability of matching enhancement results. Finally, local features with high scores that were re-extracted in the feature-sparse areas were aggregated and input to the SuperGlue network for matching, and thus, reliable matching enhancement results were obtained. The experiment selected four pairs of un-manned aerial vehicle imagery and satellite imagery that were difficult to match and compared the SD-ME algorithm with the SIFT, ContextDesc, and SuperGlue algorithms. The results revealed that the SD-ME algorithm was far superior to other algorithms in terms of the number of correct matching points, the accuracy of matching points, and the uniformity of distribution of matching points. The number of correctly matched points in each image pair increased by an average of 95.52% compared to SuperGlue. The SD-ME algorithm can effectively improve the matching quality between unmanned aerial vehicle imagery and satellite imagery and has practical value in the fields of image registration and change detection.