内点最大化与冗余点控制的无人机遥感图像配准
Remote sensing image registration of small unmanned aerial vehicles based on inlier maximization and outlier control
- 2020年24卷第11期 页码:1325-1341
纸质出版日期: 2020-11-07
DOI: 10.11834/jrs.20209060
扫 描 看 全 文
浏览全部资源
扫码关注微信
纸质出版日期: 2020-11-07 ,
扫 描 看 全 文
余蕊,陈玮扬,杨扬,杨昆,罗毅.2020.内点最大化与冗余点控制的无人机遥感图像配准.遥感学报,24(11): 1325-1341
Yu R,Chen W Y,Yang Y,Yang K and Luo Y. 2020. Remote sensing image registration of small unmanned aerial vehicles based on inlier maximization and outlier control. Journal of Remote Sensing(Chinese), 24(11):1325-1341
利用小型无人机进行遥感图像配准在自然灾害损害评估、环境监测和目标检测与追踪等领域发挥着至关重要的作用,但小型无人机的图像采集过程容易受风速/风向、复杂地形、电池容量、飞行姿态、飞行高度等自然或人为因素的影响。这些问题通常会导致捕捉到的场景重叠率低与图像非刚性畸变,在特征点提取过程中产生大量冗余点,增加了图像配准的难度。本文提出一种基于特征点的小型无人机图像配准方法,该方法的核心思想是在配准过程中识别冗余点,同时最大化可用内点数量。所识别的冗余点当作控制点,用于控制网格代图像的运动。最后通过最大化内点和合理移动控制点来恢复图像变换。本文使用50对小型无人机图像进行特征匹配和图像配准的实验,其中平均配准精度可达80.38%,并且本文方法在所有的情况下都优于5种当前流行算法。
Remote sensing image registration using small unmanned aerial vehicles plays a significant role in military and civilian fields
such as natural disaster damage assessment
ground change detection
environmental monitoring
military damage assessment
and ground target identification. This technique aims to align two or more images (i.e.
the sensed image and the reference image) of the same scene captured from different viewpoints
from different times or with different sensors. However
the imaging perspective of small unmanned aerial vehicles is vulnerable to wind speed/direction
complex terrain
battery capacity
aircraft posture
flying height
and other human factors. Such issues often cause nonrigid distortions and low overlap ratios within the captured scenes
thereby generating severe outliers during feature point extraction. Two major types of classification
namely
(1) area-based methods and (2) feature-based methods
are found in accordance with the methodological differences in current methods. We mainly focus on developing a feature-based method in this work. We introduce and discuss the current methods during the classification.
In this work
the key idea of the proposed method is to maintain a high matching ratio on inliers while using outliers for image registration. The main contributions of the proposed method are: (1) a relatively low initial threshold
which is lower than the default Scale-Invariant Feature Transform (SIFT) threshold
is usually used to extract two large sets of feature points. A putative control point processing strategy is then designed to gradually identify outliers and maximize the number of reliable inlier pairs. Dynamic SIFT threshold helps to build a coarse-to-fine transformation; (2) a local spatial structure similarity preservation is proposed to constrain the local structure of putative inliers during registration while using a global constraint to refine the warping field by coherently moving putative dummy control points. (3) a dynamic Gaussian kernel is developed to control the displacement distances of feature points such that the transformation is gradually changed from rigid to nonrigid for assisting the above coarse-to-fine search strategy.
This study considers five groups of multiview small unmanned aerial vehicle images of typical landform in different regions as the study area. The experiments on feature matching and image registration are performed using 50 pairs of small unmanned aerial vehicle images. Compared with five state-of-the-art methods (SIFT
SURF
CPD
GLMATPS
and GL-CATE)
our method demonstrates higher registration quality in all scenarios
where the viewpoint change is up to 100°
and the overlap rate is close to 0.5.
In this work
we presented a feature-based method for the remote sensing image registration of small unmanned aerial vehicles based on inlier maximization and outlier control. The key idea is to gradually identify the control points and maximize the number of available inlier pairs. The identified control points are simultaneously used to refine the warping grids within the overlap and nonoverlap areas by reasonably and coherently moving them. The image transformation is recovered by the maximized inliers and the reasonably moved control points. Extensive experiments proved that the use of outliers can improve image registration accuracy.
遥感小型无人机图像配准冗余点剔除动态SIFT阈值动态高斯核
remote sensingsmall unmanned aerial vehiclesimage registrationoutlier registeringdynamic SIFT thresholddynamic Gaussian kernel
Aguilera C, Barrera F, Lumbreras F, Sappa A D and Toledo R. 2012. Multispectral image feature points. Sensors, 12(9): 12661-12672 [DOI: 10.3390/s120912661http://dx.doi.org/10.3390/s120912661]
Basu A, Harris I R, Hjort N L and Jones M C. 1998. Robust and efficient estimation by minimising a density power divergence. Biometrika, 85(3): 549-559 [DOI: 10.1093/biomet/85.3.549http://dx.doi.org/10.1093/biomet/85.3.549]
Bay H, Ess A, Tuytelaars T and van Gool L. 2008. Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3): 346-359 [DOI: 10.1016/j.cviu.2007.09.014http://dx.doi.org/10.1016/j.cviu.2007.09.014]
Belongie S, Malik J and Puzicha J. 2002. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522 [DOI: 10.1109/34.993558http://dx.doi.org/10.1109/34.993558]
Bentoutou Y, Taleb N, Kpalma K and Ronsin J. 2005. An automatic image registration for applications in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2127-2137 [DOI: 10.1109/tgrs.2005.853187http://dx.doi.org/10.1109/tgrs.2005.853187]
Bookstein F L. 1989. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6): 567-585 [DOI: 10.1109/34.24792http://dx.doi.org/10.1109/34.24792]
Bouchiha R and Besbes K. 2013. Automatic remote-sensing image registration using SURF. International Journal of Computer Theory and Engineering, 5(1): 88-92 [DOI: 10.7763/IJCTE.2013.V5.653http://dx.doi.org/10.7763/IJCTE.2013.V5.653]
Brown L G. 1992. A survey of image registration techniques. ACM Computing Surveys, 24(4): 325-376 [DOI: 10.1145/146370.146374http://dx.doi.org/10.1145/146370.146374]
Butenuth M, Burkert F, Schmidt F, Hinz S, Hartmann, Kneidl A, Borrmann A and Sirmacek B. 2011. Integrating pedestrian simulation, tracking and event detection for crowd analysis//Proceedings of 2011 IEEE International Conference on Computer Vision Workshops. Barcelona, Spain: IEEE: 150-157 [DOI: 10.1109/ICCVW.2011.6130237http://dx.doi.org/10.1109/ICCVW.2011.6130237]
Calonder M, Lepetit V, Strecha C and Fua P. 2010. Brief: binary robust independent elementary features//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece: Springer: 778-792 [DOI: 10.1007/978-3-642-15561-1_56http://dx.doi.org/10.1007/978-3-642-15561-1_56]
Chui H L and Rangarajan A. 2003. A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding, 89(2/3): 114-141 [DOI: 10.1016/s1077-3142(03)00009-2http://dx.doi.org/10.1016/s1077-3142(03)00009-2]
Chum O and Matas J. 2005. Matching with PROSAC- progressive sample consensus//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05). San Diego, CA, USA: IEEE: 220226 [DOI: 10.1109/CVPR.2005.221]
Chung F R K. 1997. Spectral Graph Theory (CBMS Regional Conference Series in Mathematics, 92). Providence: American Mathematical Society [DOI: 10.1007/978-3-319-17566-9_12http://dx.doi.org/10.1007/978-3-319-17566-9_12]
Colomina I and Molina P. 2014. Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 92: 79-97 [DOI: 10.1016/j.isprsjprs.2014.02.013http://dx.doi.org/10.1016/j.isprsjprs.2014.02.013]
Dellinger F, Delon J, Gousseau Y, Michel J and Tupin F. 2015. SAR-SIFT: a SIFT-Like algorithm for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 453-466 [DOI: 10.1109/TGRS.2014.2323552http://dx.doi.org/10.1109/TGRS.2014.2323552]
Dempster A P, Laird N M and Rubin D B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (methodological), 39(1): 1-22 [DOI: 10.1111/j.2517-6161.1977http://dx.doi.org/10.1111/j.2517-6161.1977]
Fischler M A and Bolles R C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381-395 [DOI: 10.1145/358669.358692http://dx.doi.org/10.1145/358669.358692]
Frome A, Huber D, Kolluri R, Bülow T and Malik J. 2004. Recognizing objects in range data using regional point descriptors//Proceedings of the 8th European Conference on Computer Vision. Prague, Czech Republic: Springer: 224-237 [DOI: 10.1007/978-3-540-24672-5_18http://dx.doi.org/10.1007/978-3-540-24672-5_18]
Harris C and Stephens M. 1988. A combined corner and edge detector//Proceedings of Alvey Vision Conference. [s.l.]: Alvety Vision Club: 15(50): 10-5244 [DOI: 10.5244/C.2.23http://dx.doi.org/10.5244/C.2.23]
Jensen J R and Lulla K. 1987. Introductory digital image processing. Geocarto International, 2(1): 65 [DOI: 10.1080/1010604870935 4084http://dx.doi.org/10.1080/10106048709354084]
Jian B and Vemuri B C. 2011. Robust point set registration using gaussian mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8): 1633-1645 [DOI: 10.1109/TPAMI.2010.223http://dx.doi.org/10.1109/TPAMI.2010.223]
Jonker R and Volgenant A. 1987. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing, 38(4): 325-340 [DOI: 10.1007/bf02278710http://dx.doi.org/10.1007/bf02278710]
Kato T, Omachi S and Aso H. 2002. Asymmetric Gaussian and its application to pattern recognition//Joint IAPR International Workshops SSPR 2002 and SPR 2002 Windsor Structural, Syntactic, and Statistical Pattern Recognition. Ontario, Canada: Springer: 405-413 [DOI: 10.1007/3-540-70659-3_42http://dx.doi.org/10.1007/3-540-70659-3_42]
Ke Y and Sukthankar R. 2004. PCA-SIFT: a more distinctive representation for local image descriptors//Proceedings of 2004 IEEE Computer Vision and Pattern Recognition. Washington, DC, USA: IEEE: 506-513 [DOI: 10.1109/CVPR.2004.1315206http://dx.doi.org/10.1109/CVPR.2004.1315206]
Li J Y, Hu Q W, Ai M Y and Zhong R F. 2017. Robust feature matching via support-line voting and affine-invariant ratios. ISPRS Journal of Photogrammetry and Remote Sensing, 132: 61-76 [DOI: 10.1016/j.isprsjprs.2017.08.009http://dx.doi.org/10.1016/j.isprsjprs.2017.08.009]
Li X M, Zheng L and Hu Z Y. 2006. Sift based automatic registration of remotely-sensed imagery. Journal of Remote Sensing, 10(6): 885-892
李晓明, 郑链, 胡占义. 2006. 基于SIFT特征的遥感影像自动配准. 遥感学报, 10(6): 885-892 [DOI: 10.11834/jrs.200606130http://dx.doi.org/10.11834/jrs.200606130]
Lin H, Du P J, Zhao W C, Zhang L P and Sun H S. 2010. Image registration based on corner detection and affine transformation//Proceedings of the 3rd International Congress on Image and Signal Processing. Yantai, China: IEEE: 2184-2188 [DOI: 10.1109/CISP.2010.5647722http://dx.doi.org/10.1109/CISP.2010.5647722]
Ling H B and Jacobs D W. 2007. Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(2): 286-299 [DOI: 10.1109/TPAMI.2007.41http://dx.doi.org/10.1109/TPAMI.2007.41]
Lowe D G. 1999. Object recognition from local scale-invariant features//Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE [DOI: 10.1109/ICCV.1999.790410http://dx.doi.org/10.1109/ICCV.1999.790410]
Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91-110 [DOI: 10.1023/b:visi.0000029664.99615.94http://dx.doi.org/10.1023/b:visi.0000029664.99615.94]
Ma J Y, Jiang J J, Zhou H B, Zhao J and Guo X J. 2018a. Guided locality preserving feature matching for remote sensing image registration. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4435-4447 [DOI: 10.1109/TGRS.2018.2820040http://dx.doi.org/10.1109/TGRS.2018.2820040]
Ma J Y, Qiu W C, Zhao J, Ma Y, Yuille A L and Tu Z. 2015. Robust L2E estimation of transformation for non-rigid registration. IEEE Transactions on Signal Processing, 63(5): 1115-1129 [DOI: 10.1109/TSP.2014.2388434http://dx.doi.org/10.1109/TSP.2014.2388434]
Ma J Y, Zhao J, Guo H Q, Jiang J J, Zhou H B and Gao Y. 2017. Locality preserving matching//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne, Australia: IJCAI: 4492-4498. [DOI: 10.24963/ijcai.2017/627http://dx.doi.org/10.24963/ijcai.2017/627]
Ma J Y, Zhao J, Jiang J J, Zhou H B and Guo X J. 2019. Locality preserving matching. International Journal of Computer Vision, 127(5): 512-531 [DOI: 10.1007/s11263-018-1117-zhttp://dx.doi.org/10.1007/s11263-018-1117-z]
Ma J Y, Zhao J, Jiang J J, Zhou H B, Zhou Y, Wang Z and Guo X J. 2018b. Visual Homing via Guided Locality Preserving Matching//Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD, Australia: IEEE: 7254-7261 [DOI: 10.1109/ICRA.2018.8460935http://dx.doi.org/10.1109/ICRA.2018.8460935]
Ma J Y, Zhao J, Tian J W, Bai X and Tu Z W. 2013. Regularized vector field learning with sparse approximation for mismatch removal. Pattern Recognition, 46(12): 3519-3532 [DOI: 10.1016/j.patcog.2013.05.017http://dx.doi.org/10.1016/j.patcog.2013.05.017]
Ma J Y, Zhao J and Yuille A L. 2016. Non-rigid point set registration by preserving global and local structures. IEEE Transactions on Image Processing, 25(1): 53-64 [DOI: 10.1109/TIP.2015.2467217http://dx.doi.org/10.1109/TIP.2015.2467217]
Ma X Y, Yuan Y, Wang C Y, Chen J B and He D X. 2016. A control point uniformization algorithm for high-resolution remote sensing image registration. Remote Sensing Information, 31(3): 24-30
马旭燕, 袁媛, 汪承义, 陈静波, 贺东旭. 2016. 高分辨率遥感图像配准控制点均匀化算法. 遥感信息, 31(3): 24-30 [DOI: 10.3969/j.issn.1000-3177.2016.03.004http://dx.doi.org/10.3969/j.issn.1000-3177.2016.03.004]
Meng F Q and You F C. 2013. A tracking algorithm based on orb//Proceedings of 2013 International Conference on Mechatronic Sciences. Shengyang, China: IEEE: 1187-1190 [DOI: 10.1109/MEC.2013.6885245http://dx.doi.org/10.1109/MEC.2013.6885245]
Mikolajczyk K and Schmid C. 2005. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10): 1615-1630 [DOI: 10.1109/TPAMI.2005.188http://dx.doi.org/10.1109/TPAMI.2005.188]
Misra I, Moorthi S M, Dhar D and Ramakrishnan R. 2012. An automatic satellite image registration technique based on Harris corner detection and random sample consensus (RANSAC) outlier rejection model//Proceedings of the 1st International Conference on Recent Advances in Information Technology (RAIT). Dhanbad, India: IEEE: 68-73 [DOI: 10.1109/RAIT.2012.6194482http://dx.doi.org/10.1109/RAIT.2012.6194482]
Moisan L, Moulon P and Monasse P. 2012. Automatic homographic registration of a pair of images, with a contrario elimination of outliers. Image Processing on Line, 2: 56-73 [DOI: 10.5201/ipol.2012.mmm-ohhttp://dx.doi.org/10.5201/ipol.2012.mmm-oh]
Myronenko A and Song X B. 2010. Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12): 2262-2275 [DOI: 10.1109/TPAMI.2010.46http://dx.doi.org/10.1109/TPAMI.2010.46]
Qin Y Y, Xu H K and Chen H R. 2014. Image feature points matching via improved ORB//Proceedings of 2014 International Conference on Progress in Informatics and Computing. Shanghai, China: IEEE: 204-208 [DOI: 10.1109/PIC.2014.6972325http://dx.doi.org/10.1109/PIC.2014.6972325]
Qu H B, Wang J Q, Li B and Yu M. 2017. Probabilistic model for robust affine and non-rigid point set matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(2): 371-384 [DOI: 10.1109/TPAMI.2016.2545659http://dx.doi.org/10.1109/TPAMI.2016.2545659]
Radoi A and Datcu M. 2015. Automatic change analysis in satellite images using binary descriptors and Lloyd-max quantization. IEEE Geoscience and Remote Sensing Letters, 12(6): 1223-1227 [DOI: 10.1109/LGRS.2015.2389144http://dx.doi.org/10.1109/LGRS.2015.2389144]
Raguram R, Chum O, Pollefeys M, Matas J and Frahm J M. 2013. USAC: a universal framework for random sample consensus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8): 2022-2038 [DOI: 10.1109/tpami.2012.257http://dx.doi.org/10.1109/tpami.2012.257]
Rosten E and Drummond T. 2006. Machine learning for high-speed corner detection//Proceedings of the 9th European conference on Computer Vision. Berlin, Heidelberg: Springer: 430-443 [DOI: 10.1007/11744023_34http://dx.doi.org/10.1007/11744023_34]
Rublee E, Rabaud V, Konolige K and Bradski G. 2011. ORB: an efficient alternative to sift or surf//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE: 2564-2571 [DOI: 10.1109/ICCV.2011.6126544http://dx.doi.org/10.1109/ICCV.2011.6126544]
Sedaghat A and Ebadi H. 2015. Remote sensing image matching based on adaptive binning SIFT descriptor. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5283-5293 [DOI: 10.1109/TGRS.2015.2420659http://dx.doi.org/10.1109/TGRS.2015.2420659]
Sedaghat A, Mokhtarzade M and Ebadi H. 2011. Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4516-4527 [DOI: 10.1109/TGRS.2011.2144607http://dx.doi.org/10.1109/TGRS.2011.2144607]
Shan X J, Tang P and Zheng K. 2016. GSSAC: false matching points detection method for remote sensing images. Application Research of Computers, 33(5): 1562-1565
单小军, 唐娉, 郑柯. 2016. GSSAC: 一种用于遥感影像配准的误匹配点检测方法. 计算机应用研究, 33(5): 1562-1565 [DOI: 10.3969/j.issn.1001-3695.2016.05.062http://dx.doi.org/10.3969/j.issn.1001-3695.2016.05.062]
Sirmacek B and Reinartz P. 2013. Feature analysis for detecting people from remotely sensed images. Journal of Applied Remote Sensing, 7(1): 073594 [DOI: 10.1117/1.jrs.7.073594http://dx.doi.org/10.1117/1.jrs.7.073594]
Song Z L and Zhang J P. 2010. Remote sensing image registration based on retrofitted SURF algorithm and trajectories generated from lissajous figures. IEEE Geoscience and Remote Sensing Letters, 7(3): 491-495 [DOI: 10.1109/LGRS.2009.2039917http://dx.doi.org/10.1109/LGRS.2009.2039917]
Tola E, Lepetit V and Fua P. 2010. DAISY: an efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5): 815-830 [DOI: 10.1109/TPAMI.2009.77http://dx.doi.org/10.1109/TPAMI.2009.77]
Torr P H S and Zisserman A. 2000. MLESAC: a new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding, 78(1): 138-156 [DOI: 10.1006/cviu.1999.0832http://dx.doi.org/10.1006/cviu.1999.0832]
Vedaldi A and Fulkerson B. 2010. VLFeat: an open and portable library of computer vision algorithms//Proceedings of the 18th ACM international conference on Multimedia. Firenze, Italy: IEEE: 1469-1472 [DOI: 10.1145/1873951.1874249http://dx.doi.org/10.1145/1873951.1874249]
Wang G, Wang Z C, Chen Y F and Zhao W D. 2015. A robust non-rigid point set registration method based on asymmetric Gaussian representation. Computer Vision and Image Understanding, 141: 67-80 [DOI: 10.1016/j.cviu.2015.05.014http://dx.doi.org/10.1016/j.cviu.2015.05.014]
Wang G, Zhou Q Q and Chen Y F. 2017. Robust non-rigid Point set registration using spatially constrained Gaussian fields. IEEE Transactions on Image Processing, 26(4): 1759-1769 [DOI: 10.1109/TIP.2017.2658947http://dx.doi.org/10.1109/TIP.2017.2658947]
Wang W and Wang Z Q. 2017. Image registration of multi-scale feature points clustering. Journal of Chinese Computer Systems, 38(11): 2597-2603
王薇, 王展青. 2017. 多尺度特征点聚类的图像配准算法. 小型微型计算机系统, 38(11): 2597-2603 [DOI: 10.3969/j.issn.1000-1220.2017.11.033http://dx.doi.org/10.3969/j.issn.1000-1220.2017.11.033]
Wei Z Q, Han Y F, Li M Y, Yang K, Yang Y, Luo Y and Ong S H. 2017. A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring. Remote Sensing, 9(9): 904 [DOI: 10.3390/rs9090904http://dx.doi.org/10.3390/rs9090904]
Wu Y, Ma W P, Gong M G, Su L Z and Jiao L C. 2015. A novel point-matching algorithm based on fast sample consensus for image registration. IEEE Geoscience and Remote Sensing Letters, 12(1): 43-47 [DOI: 10.1109/LGRS.2014.2325970http://dx.doi.org/10.1109/LGRS.2014.2325970]
Yang K, Pan A N, Yang Y, Zhang S, Ong S H and Tang H L. 2017. Remote sensing image registration using multiple image features. Remote Sensing, 9(6): 581 [DOI: 10.3390/rs9060581http://dx.doi.org/10.3390/rs9060581]
Yang Y, Ong S H and Foong K W C. 2015. A robust global and local mixture distance based non-rigid point set registration. Pattern Recognition, 48(1): 156-173 [DOI: 10.1016/j.patcog.2014.06.017http://dx.doi.org/10.1016/j.patcog.2014.06.017]
Yang Z Q, Yang Y, Yang K and Wei Z Q. 2019. Non-rigid image registration with dynamic Gaussian component density and space curvature preservation. IEEE Transactions on Image Processing, 28(5): 2584-2598 [DOI: 10.1109/TIP.2018.2887204http://dx.doi.org/10.1109/TIP.2018.2887204]
Ye Y X and Shan J. 2014. A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences. ISPRS Journal of Photogrammetry and Remote Sensing, 90: 83-95. [DOI: 10.1016/j.isprsjprs.2014.01.009http://dx.doi.org/10.1016/j.isprsjprs.2014.01.009]
Yu L, Zhang D R and Holden E J. 2008. A fast and fully automatic registration approach based on point features for multi-source remote-sensing images. Computers and Geosciences, 34(7): 838-848 [DOI: 10.1016/j.cageo.2007.10.005http://dx.doi.org/10.1016/j.cageo.2007.10.005]
Yuille A L and Grzywacz N M. 1989. A mathematical analysis of the motion coherence theory. International Journal of Computer Vision, 3(2): 155-175 [DOI: 10.1007/bf00126430http://dx.doi.org/10.1007/bf00126430]
Zhang S, Yang K, Yang Y and Luo Y. 2018a. Nonrigid image registration for low-altitude SUAV images with large viewpoint changes. IEEE Geoscience and Remote Sensing Letters, 15(4): 592-596 [DOI: 10.1109/LGRS.2018.2796136http://dx.doi.org/10.1109/LGRS.2018.2796136]
Zhang S, Yang Y, Yang K, Luo Y and Ong S H. 2017. Point set registration with global-local correspondence and transformation estimation//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 1150 [DOI: 10.1109/ICCV.2017.29]
Zhang S, Yang K, Yang Y, Luo Y and Wei Z Q. 2018b. Non-rigid point set registration using dual-feature finite mixture model and global-local structural preservation. Pattern Recognition, 80: 183-195 [DOI: 10.1016/j.patcog.2018.03.004http://dx.doi.org/10.1016/j.patcog.2018.03.004]
Zhao M, An B W, Wu Y P and Lin C Q. 2013. Bi-SOGC: a graph matching approach based on bilateral KNN spatial orders around geometric centers for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 10(6): 1429-1433 [DOI: 10.1109/LGRS.2013.2259612http://dx.doi.org/10.1109/LGRS.2013.2259612]
Zhu Y and Fujimura K. 2003. Driver face tracking using Gaussian mixture model (GMM)//Proceedings of 2003 IEEE Intelligent Vehicles Symposium. Columbus, OH, USA: IEEE: 587-592 [DOI: 10.1109/IVS.2003.1212978http://dx.doi.org/10.1109/IVS.2003.1212978]
Zitová B and Flusser J. 2003. Image registration methods: a survey. Image and Vision Computing, 21(11): 977-1000 [DOI: 10.1016/S0262-8856(03)00137-9http://dx.doi.org/10.1016/S0262-8856(03)00137-9]
相关作者
相关机构