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基于改进KAZE的无人机航拍图像拼接算法

韩敏 闫阔 秦国帅

韩敏, 闫阔, 秦国帅. 基于改进KAZE的无人机航拍图像拼接算法. 自动化学报, 2019, 45(2): 305-314. doi: 10.16383/j.aas.2018.c170521
引用本文: 韩敏, 闫阔, 秦国帅. 基于改进KAZE的无人机航拍图像拼接算法. 自动化学报, 2019, 45(2): 305-314. doi: 10.16383/j.aas.2018.c170521
HAN Min, YAN Kuo, QIN Guo-Shuai. A Mosaic Algorithm for UAV Aerial Image With Improved KAZE. ACTA AUTOMATICA SINICA, 2019, 45(2): 305-314. doi: 10.16383/j.aas.2018.c170521
Citation: HAN Min, YAN Kuo, QIN Guo-Shuai. A Mosaic Algorithm for UAV Aerial Image With Improved KAZE. ACTA AUTOMATICA SINICA, 2019, 45(2): 305-314. doi: 10.16383/j.aas.2018.c170521

基于改进KAZE的无人机航拍图像拼接算法

doi: 10.16383/j.aas.2018.c170521
基金项目: 

国家自然科学基金委科学仪器基础研究专项 51327004

国家自然科学基金 61773087

国家自然科学基金 61702077

中央高校基本科研业务费 DUT17ZD216

详细信息
    作者简介:

    闫阔  大连理工大学电子信息与电气工程学部硕士研究生.主要研究方向为图像拼接技术.E-mail:yankuo@mail.dlut.edu.cn

    秦国帅  大连理工大学建设工程学部博士研究生.主要研究方向为环境水资源系统分析.E-mail:qgs1991@mail.dlut.edu.cn

    通讯作者:

    韩敏  大连理工大学电子信息与电气工程学部教授.主要研究方向为模式识别, 复杂系统建模与分析及时间序列预测.本文通信作者.E-mail:minhan@dlut.edu.cn

A Mosaic Algorithm for UAV Aerial Image With Improved KAZE

Funds: 

Special Fund for Basic Research on Scientiflc Instruments of National Natural Science Foundation of China 51327004

National Natural Science Foundation of China 61773087

National Natural Science Foundation of China 61702077

Fundamental Research Funds for the Central Universities DUT17ZD216

More Information
    Author Bio:

     Master student at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. His main research interest is image stitching

     Ph. D. candidate at the Faculty of Infrastructure Engineering, Dalian University of Technology. His main research interest is system analysis of environment and water resources

    Corresponding author: HAN Min  Professor at the Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. Her research interest covers pattern recognition, modeling and analysis of complex system, and time series prediction. Corresponding author of this paper
  • 摘要: 为了更好地解决航拍图像易受光照、旋转变化、尺度变化等影响,KAZE算法实时性较差以及基于K近邻的特征匹配算法耗时较长等问题,该文提出了一种基于改进KAZE的无人机航拍图像拼接算法.该方法首先利用加速的KAZE算法提取图像的特征点,采用二进制特征描述子FREAK(Fast retina keypoint)进行特征点描述,然后使用Grid-KNN算法进行特征点粗匹配,利用随机一致性算法对匹配的特征点进一步提纯并计算几何变换模型,最后采用加权平均算法对图像进行融合.实验结果表明,该文所提算法使图像在光照变化、旋转变化及尺度变化下具有较好的性能,且处理速度较KAZE算法与K近邻特征匹配算法有较大提升,是一种稳定、精确度高、拼接效果良好的无人机航拍图像拼接方法.
    1)  本文责任编委 左旺孟
  • 图  1  FREAK算法采样模式

    Fig.  1  FREAK algorithm sampling mode

    图  2  FREAK算法主方向确定

    Fig.  2  Decision orientation of FREAK

    图  3  Grid-KNN匹配区域示意图

    Fig.  3  The diagram of Grid-KNN matching area

    图  4  基于改进KAZE算法无人机航拍图像拼接流程

    Fig.  4  The process of UAV aerial image mosaic based on improved KAZE algorithm

    图  5  实验数据

    Fig.  5  Experimental data

    图  6  Leuven数据和Boat数据匹配正确率比较

    Fig.  6  The comparison of correct matching rate for Leuven data and Boat data

    图  7  Leuven数据和Boat数据匹配精度比较

    Fig.  7  The comparison of matching accuracy for Leuven data and Boat data

    图  8  图像配准结果

    Fig.  8  The results of matching

    图  9  图像拼接结果

    Fig.  9  The results of stitching

    表  1  特征点提取平均用时比较(ms)

    Table  1  The comparison of feature point extraction average time (ms)

    图像编号 SIFT ORB KAZE 本文算法
    Leuven 2.184 0.032 2.331 0.941
    Boat 2.965 0.036 2.564 0.783
    图 5 (a) 5.586 0.216 5.541 2.908
    图 5 (b) 8.238 0.222 6.000 3.748
    图 5 (c) 4.161 0.151 5.247 2.726
    下载: 导出CSV

    表  2  特征匹配平均用时比较(ms)

    Table  2  The comparison of feature matching average time (ms)

    图像编号 SIFT ORB KAZE 本文算法
    Leuven 0.431 0.268 0.305 0.174
    Boat 1.097 0.530 0.291 0.253
    图 5 (a) 0.992 0.460 1.205 0.324
    图 5 (b) 1.159 0.359 1.560 0.239
    图 5 (c) 1.200 0.331 1.242 0.219
    下载: 导出CSV

    表  3  匹配正确率比较

    Table  3  The comparison of correct matching rate

    图像编号 算法 匹配点对数 正确点对数 CMR (%)
    SIFT 1 492 1 359 91.15
    图 5 (a) ORB 2 253 2 092 81.94
    KAZE 1 427 1 436 99.93
    本文算法 1 358 1356 99.85
    SIFT 1 212 1 063 87.71
    图 5 (b) ORB 1 340 995 74.25
    KAZE 1 415 1 294 91.45
    本文算法 1 280 1 159 90.55
    SIFT 1 527 1 464 95.87
    图 5 (c) ORB 1 573 1 130 71.84
    KAZE 1 438 1 392 96.80
    本文算法 1 285 1 222 95.12
    下载: 导出CSV

    表  4  配准精度比较

    Table  4  The comparison of matching accuracy

    图像编号 SIFT ORB KAZE 本文算法
    图 5 (a) 0.443 1.530 0.356 0.367
    图 5 (b) 0.509 1.729 0.446 0.489
    图 5 (c) 0.158 1.200 0.124 0.152
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-09-13
  • 录用日期:  2018-01-20
  • 刊出日期:  2019-02-20

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