高技术通讯2025，Vol.35Issue(10)：1088-1099,12.DOI:10.3772/j.issn.1002-0470.2025.10.006
无人机影像Robert特征增强拼接算法
Robert feature enhanced stitching algorithm for drone images
摘要
Abstract
In this paper,a Robert feature enhanced kernel-based accelerated feature extraction(KAZE)stitching algo-rithm is proposed to address the issues of homography error accumulation and building misalignment ghosting in drone image stitching.The gradient operation is performed before image stitching,the detected edges is overlayed with the original gray image,and the information entropy is chosen to filter the optimal gradient calculation method.After feature enhancement preprocessing,the left and right images are stitched using dual transformations of left af-fine and right perspective translation.The concatenated images are subjected to progressive gradual out fusion pro-cessing to achieve natural pixel transitions and uniform lighting and color processing.Experimental results show that among the five feature detection methods Harris,scale-invariant feature transform(SIFT),speeded up robust features(SURF),shi-Tomasi,and KAZE the KAZE,SIFT,and SURF algorithms are with similar detection accuracy.However,in terms of feature matching,the correct matching rate of KAZE for feature point pair is signif-icantly higher than that of SIFT and SURF,indicating it is with high universality and robustness.The problem of misplaced ghosting in building stitching is solved in using the Robert enhanced KAZE algorithm.It reduces the ac-cumulation errors of homography,maintains the average gradient of the image,and is suitable for continuous stitc-hing drone images in large scene.关键词
KAZE算法/图像信息熵/特征匹配/随机抽样一致性/Robert算子Key words
kernel-based accelerated feature extraction algorithm/image information entropy/feature matc-hing/random sample consensus/Robert operator引用本文复制引用
刘艳,武广臣..无人机影像Robert特征增强拼接算法[J].高技术通讯,2025,35(10):1088-1099,12.基金项目
辽宁省教育厅基本科研(LJ212411430006)资助项目. （LJ212411430006）
