| 剔除无人机影像BRISK特征误匹配点对算法 |
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| 引用本文： | 何志伟,唐伯惠,王涛,王晓红,于伯华,李闯,邓仕雄.剔除无人机影像BRISK特征误匹配点对算法[J].测绘通报,2021,0(10):78-82. |
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| 作者姓名： | 何志伟 唐伯惠 王涛 王晓红 于伯华 李闯 邓仕雄 |
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| 作者单位： | 1. 贵州省测绘产品质量监督检验站, 贵州 贵阳 550004;2. 昆明理工大学, 云南 昆明 650093;3. 中国 科学院地理科学与资源研究所, 北京 100101;4. 贵州大学林学院, 贵州 贵阳 550025;5. 烟台职业学院, 山东 烟台 264670;6. 贵州水利水电职业技术学院, 贵州 贵阳 551400 |
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| 基金项目： | 国家自然科学基金（41171079） |
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| 摘 要： | 针对BRISK特征检测算法在遥感影像中匹配时同名点对冗余度高和全局性差等特点，考虑BRISK特征检测算法能获取大量无人机遥感影像特征点，Delaunay三角网算法能够利用影像的BRISK特征点的粗匹配点对构建三角网，本文综合两种算法的优点，提出了一种结合BRISK特征检测算法和Delaunay三角网算法的剔除无人机遥感影像误匹配点对方法。该方法利用两张影像的BRISK粗匹配特征点构建Delaunay三角网，利用遍历两张影像三角网中的三角形相似度剔除错误匹配点对，并利用摄影不变量原理进一步剔除误匹配点对，提高了两张影像的精度；对比分析了Delaunay三角网的射影不变量算法，RANSAC算法分别剔除原始影像组、加入椒盐噪声影像组及旋转影像组的BRISK特征误匹配点对的效果。试验结果表明，3组影像分别利用结合BRISK特征和Delaunay三角网的射影不变量算法的无人机遥感影像匹配方法获得的正确特征匹配点对冗余度低、全局性优。
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| 关 键 词： | 无人机遥感影像 BRISK特征 RANSAC算法 Delaunay三角网 摄影不变量 三角形相似度 |
| 收稿时间： | 2020-08-28 |
An algorithm for eliminating mismatching point pairs of BRISK features in UAV images |
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| HE Zhiwei,TANG Bohui,WANG Tao,WANG Xiaohong,YU Bohua,LI Chuang,DENG Shixiong.An algorithm for eliminating mismatching point pairs of BRISK features in UAV images[J].Bulletin of Surveying and Mapping,2021,0(10):78-82. |
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| Authors: | HE Zhiwei TANG Bohui WANG Tao WANG Xiaohong YU Bohua LI Chuang DENG Shixiong |
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| Abstract: | In view of the high redundancy and poor globality of the same-name point pairs when the BRISK feature detection algorithm matches in remote sensing images, this paper considers that the BRISK feature detection algorithm can obtain a large number of UAV image feature points, and the Delaunay triangulation algorithm can use the rough matching point pairs of the BRISK feature points of the image construct a triangulation network. Combining the advantages of the two algorithms, a method combining the BRISK feature detection algorithm and the Delaunay triangulation algorithm to eliminate mismatched point pairs of UAV images is proposed. This method uses the BRISK rough matching feature points of the two images to construct the Delaunay triangulation, uses the triangle similarity in the traversal of the two images to eliminate the mismatching point pairs, and then uses the photographic invariant principle to further eliminate the wrong matching point pairs, improving the accuracy of the image matching. This paper compares and studies the effect of the projective invariant algorithm of Delaunay triangulation and the RANSAC algorithm to eliminate the original image group, adds the pepper-salt noise image group and the rotated image group to the effect of the BRISK feature mismatch point pairs. The experimental results show that the three sets of images respectively use the UAV remote sensing image matching method combining the BRISK feature and the Delaunay triangulation's projective invariant algorithm to obtain the correct feature matching points with low redundancy and excellent global performance. |
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| Keywords: | UAV remote sensing images BRISK feature RANSAC algorithm Delaunay tri-angulation projective invariants triangle similarity |
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