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An Image Matching Method for Season-changing UAV Aerial Images and Satellite Images

  • REN Yan 1 ,
  • LIU Shengnan 2 ,
  • CHEN Xinyu 1 ,
  • HUANG Zhenjia 2
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  • 1 School of Artificial Intelligence,Shenyang Aerospace University,Shenyang 110136,Liaoning,China
  • 2 School of Automation,Shenyang Aerospace University,Shenyang 110136,Liaoning,China

Received date: 2023-05-07

  Online published: 2024-12-30

Abstract

Image matching plays a vital role in UAV visual navigation. There have been many excellent image matching algorithms in this field, but there are few reports on heterogeneous image matching in season-changing. In order to further expand the current research work in this field, an image matching algorithm based on fuzzy information granules is proposed to solve the problem of heterogeneous image matching in season-changing. Firstly, according to the ground resolution of the satellite image and the UAV aerial image, the scaling factor of the UAV aerial image is determined, and then the scale preprocessing of the UAV aerial image is carried out; Secondly, an image matching method is established based on fuzzy information granules, and similarity matching is performed on the extracted edges to obtain the matching position between the UAV aerial image and the satellite image; Finally, validation is performed on winter and summer UAV aerial imagery and satellite imagery datasets. Experimental results show that the algorithm has strong robustness, improves image matching accuracy, and provides a new idea for UAV visual localization.

Cite this article

REN Yan , LIU Shengnan , CHEN Xinyu , HUANG Zhenjia . An Image Matching Method for Season-changing UAV Aerial Images and Satellite Images[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2023 , 43(5) : 16 -24 . DOI: 10.15892/j.cnki.djzdxb.2023.05.003

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[1]
SHEN J, WANG S, ZHAI Y, et al. Cooperative relative navigation for multi-UAV systems by exploiting GNSS and peer-to-peer ranging measurements[J]. IET Radar, Sonar & Navigation, 2021, 15(1): 21-36.

[2]
MAO J, ZHANG L L, HE X F, et al. Precise visual-inertial localization for UAV with the aid of a 2D georeferenced map[J]. Computing Research Repository, 2021, 2017: 05851.

[3]
PETRIOLI E, LECCESE F, LECCISI M. Inertial navigation systems for UAV: uncertainty and error measurements[C]// IEEE. 2019 IEEE 5th International Workshop on Metrology for Aero-space (Metro-Aerospace). New York: IEEE, 2019: 1-5.

[4]
ARAFAT M Y, ALAM M M, MOH S. Vision-based navigation techniques for unmanned aerial vehicles: review and challenges[J]. Drones, 2023, 7(2): 89-130.

[5]
赵春晖, 周跌慧, 林钊, 等. 无人机景像匹配视觉导航技术综述[J]. 中国科学: 信息科学, 2019, 49(5): 507-519.

ZHAO C H, ZHOU D H, LIN Z, et al. Overview of scene matching visual navigation technology for unmanned aerial vehicles[J]. Science China: Information Sciences, 2019, 49(5): 507-519.

[6]
CUI Z J, Q W F, LIU Y X. A fast image template matching algorithm based on normalized cross correlation[J]. Journal of Physics, 2020(1): 2163-2169.

[7]
CHEN S J, ZHENG S Z, XU Z G, et al. An improved image matching method based on SURF algorithm[J]. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2018, 42(3): 179-184.

[8]
BAY H, TUYTELAARS T, VAN-GOOL L. Surf: speeded up robust features[J]. Lecture Notes in Computer Science, 2006, 3951: 404-417.

[9]
LIU X, LI J B, PAN J S, et al. Image-matching framework based on region partitioning for target image location[J]. Telecommunication Systems, 2020, 74: 269-286.

[10]
MANTELLI M, PITTOL D, NEULAND R, et al. A novel measurement model based on abBRIEF for global localization of a UAV over satellite images[J]. Robotics and Autonomous Systems, 2019, 112: 304-319.

[11]
CALONDER M, LEPETIT V, STRECHA C, et al. Brief: binary robust independent elementary features[C]// Springer. Computer Vision-ECCV 2010: 11th European Conference on Computer Vision. Heraklion: Springer, Berlin, Heidelberg, 2010: 778-792.

[12]
FAN D, YANG D, ZHANG Y. Satellite image matching method based on deep convolutional neural network[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2): 90-100.

DOI

[13]
KINNARI J, VERDOJA F, KYRKI V. Season-invariant GNSS-denied visual localization for UAVs[J]. IEEE Robotics and Automation Letters, 2022, 7(4): 10232-10239.

[14]
MUSTAFA O, DERCON G, EVANGELISTA H, et al. Monitoring proglacial geomorphological landforms with unmanned aerial vehicles (UAVs)[C]// EGU. EGU General Assembly Conference Abstracts. Vienna: EGU, 2018: 10025.

[15]
朱国涛, 曹建平. 基于OpenGL和谷歌地图的电子地图实时绘制方法研究[J]. 软件工程与应用, 2019, 8: 155-161.

ZHU G T, CAO J P. Research on real time drawing method of electronic map based on OpenGL and Google maps[J]. Software Engineering and Applications, 2019, 8: 155-161.

[16]
杨润书, 马燕燕, 殷海舟. 低空无人机航摄系统地面分辨率与航高的关系研究[J]. 地矿测绘, 2013(3): 1-2.

YANG R S, MA Y Y, YIN H Z. Research on the relationship between plane resolution and flight height of low-altitude UAV aerial photography system[J]. Geological and Mineral Surveying and Mapping, 2013(3): 1-2.

[17]
CEBECAUER T, SURI M. Exporting geospatial data to web tiled map services using GRASS GIS[J]. OSGeo Journal, 2008, 5: 1-7.

[18]
BOIXADER D, RECASENS J. Vague and fuzzy t-norms and t-conorms[J]. Fuzzy Sets and Systems, 2022, 433: 156-175.

[19]
修保新, 吴孟达. 图像模糊信息粒的适应性度量及其在边缘检测中的应用[J]. 电子学报, 2004, 32(2): 274-277.

XIU B X, WU M D. Adaptability measure to fuzzy information granule on image and its application to edge-detection[J]. Acta Electronica Sinica, 2004, 32(2): 274-277.

[20]
XIU B, ZHANG W, LIU Z, et al. Complementary image compression based on the theory of fuzzy information granulation[C]// IEEE. 2005 IEEE International Conference on Systems, Man and Cybernetics. New York: IEEE, 2005, 4: 3024-3029.

[21]
BRAGA J R G, VELHO H F C, CONTE G, et al. An image matching system for autonomous UAV navigation based on neural network[C]// IEEE. 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV). New York: IEEE, 2016: 1-6.

[22]
NG P C, HENIKOFF S. SIFT: Predicting amino acid changes that affect protein function[J]. Nucleic Acids Research, 2003, 31(13): 3812-3814.

DOI PMID

[23]
CHANTARA W, MUN J H, SHIN D W, et al. Object tracking using adaptive template matching[J]. IEIE Transactions on Smart Processing and Computing, 2015, 4(1): 1-9.

[24]
HISHAM M B, YAAKOB S N, RAOF R A A, et al. Template matching using sum of squared difference and normalized cross correlation[C]// IEEE. 2015 IEEE Student Conference on Research and Development (SCOReD). New York: IEEE, 2015: 100-104.

[25]
孙延坤, 李彩林, 王佳文, 等. 融合绝对误差和与Census变换的双目立体图像匹配算法[J]. 科学技术与工程, 2020, 20(29): 12035-12041.

SUN Y K, LI C L, WANG J W, et al. Binocular stereo image matching algorithm combining absolute error and census transformation[J]. Science Technology and Engineering, 2020, 20(29): 12035-12041.

[26]
HAN X, LEUNG T, JIA Y, et al. Matchnet: unifying feature and metric learning for patch-based matching[C]// IEEE. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2015: 3279-3286.

[27]
LI C, LIU G, YUAN Y. A multi-source image matching network for UAV visual location[C]// IEEE. 2022 IEEE International Conference on Image Processing(ICIP). New York: IEEE, 2022: 1651-1655.

[28]
DEKEL T, ORON S, RUBINSTEIN M, et al. Best-buddies similarity for robust template matching[C]// IEEE. Proceedings of the IEEE conference on computer vision and pattern recognition. New York: IEEE, 2015: 2021-2029.

[29]
DAI W, CHANG T, ZHANG L. Template matching based on deformation diversity similarity and scale filtering[C]// IEEE. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). New York: IEEE, 2020: 704-708.

[30]
BODUR M, MEHROLHASSANI M. Satellite images-based obstacle recognition and trajectory generation for agricultural vehicles[J]. International Journal of Advanced Robotic Systems, 2015, 12(12): 188-198.

[31]
REN Y, WANG Z. A novel scene matching algorithm via deep learning for vision-based UAV absolute localization[C]// IEEE. 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM). New York: IEEE, 2022: 211-218.

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