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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (1) : 93-100     DOI: 10.6046/gtzyyg.2016.01.14
Technology and Methodology |
UAV image matching based on CSIFT feature
GENG Juan1, HE Chenglong2, LIU Xianxin2
1. Joint Remote Sensing Research Centre for Rail Transit Project, Southwest Jiaotong University, Chengdu 610031, China;
2. Faculty of Earth and Engineering, Southwest Jiaotong University, Chengdu 610031, China
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Abstract  

With the development of the unmanned aerial vehicle(UAV)technique, currently UAV data have been gradually used in application and research fields. Differing from the current UAV matching method that integrates Photogrammetry and Image control points, the novel method presented in this paper is based on the color scale-invariant feature transform (CSIFT) feature to collect the key match points in the reference image and the image to be matched, followed by using the random sample consensus algorithm (RANSAC)method to extract the match points for the final matching result. The authors use an example to verify the feasibility and the validity of the method. Compared with the SIFT method, the accuracy could be increased from 70% to 88%, and the algorithm proposed in this paper can not only guarantee the matching result but also produce less matching points and use less working time.

Keywords microwave property      SAR      database      interpretation key     
:  TP751.1  
Issue Date: 27 November 2015
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BIAN Xiaolin
SHAO Yun
ZHANG Fengli
FU Xiyou
Cite this article:   
BIAN Xiaolin,SHAO Yun,ZHANG Fengli, et al. UAV image matching based on CSIFT feature[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 93-100.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.01.14     OR     https://www.gtzyyg.com/EN/Y2016/V28/I1/93

[1] 张永军.无人驾驶飞艇低空遥感影像的几何处理[J].武汉大学学报:信息科学版,2009,34(3):284-288. Zhang Y J.Geometric processing of low altitude remote sensing images captured by unmanned airship[J].Geomatics and Information Science of Wuhan University,2009,34(3):284-288.

[2] 苏俊英.SIFT特征匹配无人飞艇多光谱影像拼接[J].应用科学学报,2010,28(6):616-620. Su J Y.Mosaicing of multiple spectrum images acquired from unmanned airship with SIFT feature matching[J].Journal of Applied Sciences,2010,28(6):616-620.

[3] 狄颖辰,陈云坪,陈莹莹,等.无人机影像拼接算法综述[J].计算机应用,2011,31(1):170-174. Di Y C,Chen Y P,Chen Y Y,et al.Survey on image mosaic algorithm of unmanned aerial vehicle[J].Journal of Computer Applications,2011,31(1):170-174.

[4] 王斌,王伟锋.一种基于局部灰度匹配的无人机图像拼接算法[J].中国石油大学学报:自然科学版,2009,33(2):169-173. Wang B,Wang W F.Unmanned aerial vehicle image mosaic algorithm based on local gray fitting[J].Journal of China University of Petroleum:Edition of Natural Science,2009,33(2):169-173.

[5] 白廷柱,侯喜报.基于SIFT算子的图像匹配算法研究[J].北京理工大学学报,2013,33(6):622-627. Bai T Z,Hou X B.An improved image matching algorithm based on SIFT[J].Transactions of Beijing Institute of Technology,2013,33(6):622-627.

[6] 唐敏,李永树,鲁恒.无人机影像的同名点匹配[J].光电工程,2012,39(3):19-24. Tang M,Li Y S,Lu H.Correspondence points matching of UAV images[J].Opto-Electronic Engineering,2012,39(3):19-24.

[7] 刘立,彭复员,赵坤,等.采用简化SIFT算法实现快速图像匹配[J].红外与激光工程,2008,37(1):181-184. Liu L,Peng F Y,Zhao K,et al.Simplified SIFT algorithm for fast image matching[J].Infrared and Laser Engineering,2008,37(1):181-184.

[8] 吴寅初,马戎.基于彩色信息的尺度不变特征变换图像特征点提取与匹配[J].计算机应用,2011,31(4):1024-1026. Wu Y C,Ma R.Image feature extraction and matching of color-based scale-invariant feature transform[J].Journal of Computer Applications,2011,31(4):1024-1026.

[9] Geusebroek J M,van den Boomgaard R,Smeulders A W M,et al.Color invariance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(12):1338-1350.

[10] Brown M,Lowe D G.Invariant features from interest point groups[C]//Proceedings of the British Machine Vision Conference.Cardiff,UK:[s.n.],2002:656-665.

[11] Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.

[12] 王金泉,李钦富.基于单应性矩阵的SAR图像配准技术研究[J].中国电子科学研究院学报,2009,3(6):657-660. Wang J Q,Li Q F.Research on SAR image registration based on homograph matrix[J].Journal of CAEIT,2009,3(6):657-660.

[13] Fischler M A,Bolles R C.Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395.

[14] Szeliski R.Video mosaics for virtual environments[J].IEEE Computer Graphics and Applications,1996,16(2):22-30.

[15] 王娟,师军,吴宪祥.图像拼接技术综述[J].计算机应用研究,2008,25(7):1940-1943. Wang J,Shi J,Wu X X.Survey of image mosaics techniques[J].Application Research of Computers,2008,25(7):1940-1943.

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