地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (3): 522-532.doi: 10.12082/dqxxkx.2022.210394
张昆1(), 王涛1,*(
), 张艳1, 郑迎辉1, 赵祥1, 李芳芳2
收稿日期:
2021-07-14
修回日期:
2021-09-25
出版日期:
2022-03-25
发布日期:
2022-05-25
通讯作者:
*王 涛(1975— ),男,河南郑州人,教授,从事数字摄影测量方向、数字图像处理等研究。E-mail: wangtaoynl@163.com作者简介:
张 昆(1997— ),男,河南兰考人,硕士,主要从事计算机视觉,三维重建等研究。E-mail: 18839103702@163.com
基金资助:
ZHANG Kun1(), WANG Tao1,*(
), ZHANG Yan1, ZHENG Yinghui1, ZHAO Xiang1, LI Fangfang2
Received:
2021-07-14
Revised:
2021-09-25
Online:
2022-03-25
Published:
2022-05-25
Supported by:
摘要:
特征匹配是面阵摆扫式航空影像处理的关键步骤,针对传统特征匹配方法在面阵摆扫式航空影像匹配时存在匹配点数量少,分布不匀均的问题,本文提出一种基于自适应亮度空间的特征匹配方法。首先根据影像POS(Postion Oriental System)信息求解待匹配影像间变换关系进行影像校正,在校正后的影像上构建自适应亮度空间,使用ORB算子和BEBLID算法在亮度空间上获取特征点和二进制特征描述符,然后基于汉明距离获取初始匹配点,使用RANSAC算法剔除粗差,最后将匹配点变换到原始影像上得到最终匹配结果。本文选取6组具有视角差异及亮度变化的面阵摆扫式航空影像进行实验,将本文算法与SIFT、SURF、ORB、ORB+BEBLID、ASIFT等匹配方法进行比较,结果表明:本文算法通过建立影像间变换关系,构建自适应亮度空间,使得算法提取的特征点数量增加1.5倍,获取匹配点数量是其他算法的3倍以上,且匹配点分布更加均匀,匹配效率高于其他算法,验证了本文算法在具有亮度变化及视角差异的面阵摆扫式航空影像上匹配的有效性。
张昆, 王涛, 张艳, 郑迎辉, 赵祥, 李芳芳. 一种基于面阵摆扫式航空影像的特征匹配方法[J]. 地球信息科学学报, 2022, 24(3): 522-532.DOI:10.12082/dqxxkx.2022.210394
ZHANG Kun, WANG Tao, ZHANG Yan, ZHENG Yinghui, ZHAO Xiang, LI Fangfang. A Feature Matching Method based on Area Array Swing-Scan Aerial Image[J]. Journal of Geo-information Science, 2022, 24(3): 522-532.DOI:10.12082/dqxxkx.2022.210394
表1
实验影像数据描述
实验影像 | 影像分辨率/像元 | 焦距/mm | GSD/cm | 倾斜角/。 | 航高/m |
---|---|---|---|---|---|
a组影像 | 4864×3232 | 300 | 5.6/6.4 | 16.9/-32.6 | 2190/2190 |
b组影像 | 4864×3232 | 300 | 5.4/5.8 | 3.8/19.6 | 2200/2210 |
c组影像 | 4864×3232 | 300 | 6.7/6.8 | 40.2/27.8 | 2197/2207 |
d组影像 | 4864×3232 | 300 | 5.3/5.5 | 3.1/13.8 | 2197/2207 |
e组影像 | 4864×3232 | 300 | 5.3/6.2 | -4.8/24.8 | 2107/2210 |
f组影像 | 4864×3232 | 300 | 5.3/6.3 | -3.6/29.8 | 2210/2207 |
表2
特征匹配数量统计
分组 | 算法 | 左/右影像特征点/个 | 最终匹配点对/个 | 匹配效率/% |
---|---|---|---|---|
e | SIFT | 1062/3159 | 38 | 3.5 |
SURF | 5990/12 260 | 67 | 1.1 | |
ORB | 2825/15 362 | 90 | 3.1 | |
ORB+BEBLID | 2825/15 362 | 51 | 1.8 | |
ASIFT | 13 624/123 688 | 175 | 0.5 | |
本文算法 | 37 800/40 631 | 1363 | 3.6 | |
f | SIFT | 3132/1649 | 46 | 2.7 |
SURF | 14 166/9627 | 51 | 0.5 | |
ORB | 16 128/8512 | 72 | 0.8 | |
ORB+BEBLID | 16 238/8512 | 96 | 1.1 | |
ASIFT | 80 640/10 588 | 151 | 1.4 | |
本文算法 | 23 261/12 186 | 863 | 7.0 |
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