Image registration algorithm based on SIFT and potential function adjusting location of points

doi:10.6046/gtzyyg.2015.03.07

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Abstract Scale invariant feature transform(SIFT) is a popular feature extraction algorithm that has applied to remote sensing image automatic registration; nevertheless, there exists a problem in the remote sensing image automatic registration based on SIFT algorithm, i.e., the distribution of feature points is always nonuniform. An automatic image registration algorithm based on potential function model is presented in this paper, which can solve the problem of optimizing nonuniformity in feature point distribution in SIFT. By adjusting the threshold of SIFT, the number of matching points is promoted. The algorithm can optimize the uniformity in feature point distribution by potential model function in molecular mechanics, and make the low-precision feature point to the sparse area of feature points. Then it revises local mutual information to improve matching point accuracy, so as to realize a high quality (uniform space distribution, high accuracy of Sub-Pixel registration) automatic image registration finally.

Keywords biomass burning particulate matter with particle size less than or equal to 10 microns (PM₁₀) aerosol optical depth (AOD) trajectory analysis

:	TP751.1
	TP391.41

Issue Date: 23 July 2015

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	FAN Dongfu
	YANG Shuyun
	WU Biwen
	FAN Dongqing
	WANG Jun
	JIANG Bo
	CHEN Xiaolong

Cite this article:

FAN Dongfu,YANG Shuyun,WU Biwen, et al. Image registration algorithm based on SIFT and potential function adjusting location of points[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 36-41.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.03.07 OR https://www.gtzyyg.com/EN/Y2015/V27/I3/36

[1] 张云生,邹峥嵘.基于改进ORB算法的遥感图像自动配准方法[J].国土资源遥感,2013,25(3):20-24.doi:10.6046/gtzyyg.2013.03.04. Zhang Y S,Zou Z R.Automatic registration method for remote sensing images based on improved ORB algorithm[J].Remote Sensing for Land and Resources,2013,25(3):20-24.doi:10.6046/gtzyyg.2013.03.04.
[2] 王晓华,邓喀中,杨化超.集成互补不变特征的SAR影像自动配准[J].国土资源遥感,2014,26(1):52-56.doi:10.6046/gtzyyg.2014.01.10. Wang X H,Deng K Z,Yang H C.Automatic SAR image registration of integrated complementary invariant feature[J].Remote Sensing for Land and Resources,2014,26(1):52-56.doi:10.6046/gtzyyg.2014.01.10.
[3] 吴伟交,王敏,黄心汉,等.基于向量夹角的SIFT特征点匹配算法[J].模式识别与人工智能,2013,26(1):123-127. Wu W J,Wang M,Huang X H,et al.SIFT feature matching algorithm based on vector angle[J].Pattern Recognition and Artificial Intelligence,2013,26(1):123-127.
[4] 张谦,贾永红,胡忠文.多源遥感影像配准中的SIFT特征匹配改进[J].武汉大学学报:信息科学版,2013,38(4):456-459. Zhang Q,Jia Y H,Hu Z W. An improved SIFT algorithm for multi-source remote sensing image registration[J].Geomativs and Information Science of Wuhan University,2013,38(4):456-459.
[5] Harris C G,Stephens M J.A combined corner and edge etector[C]//Proc Fourth Alvey Vision Conference,Manchester,U K,1988.
[6] Smith S M,Brady J M.SUSAN-A new approach to low level image processing[J].International Journal of Computer Vision,1997(1):45-78.
[7] David G L.Object recognition from local scale-invariant features[C]//Proceedings of the International Conference on Computer Vision,Corfu:1999.
[8] 陈爱军,徐光祐,史元春,基于城市航空立体像对的全自动3维建筑物建模[J].测绘学报,2002,31(1):55-57. Chen A J,Xu G Y,Shi Y C.Automated 3D building modeling based on urban aerial stereopair[J].Acta Geodaetica et Cartographic Sinica,2002,31(1):55-57.
[9] Jonathan H.SPOT digital elvation model(DEM)creation using the Otto and Chau Method[DB/OL].[2004-09]http://www.jon-claudi.co.uk/ee/index.php/geofiction/extended_text/spot_digital_elevation_model_dem_cration_using_the_otto_and_chau_method/.
[10] Mikolajczyk K,Schmid C.Scale and affine invariant interest point detectors[J].International Journal of Computer Vision,2004,60(1):63-86.
[11] 胡东红,李德华,王祖喜.均匀性度量的势函数模型[J].数学物理学报,2003,23,A(5):607-612. Hu D H,Li D H,Wang Z X.Potential function model of uniformity measurement[J].Acta Mathematiea Scientia,2003,23,A(5):607-612.
[12] 蔡文生,林翼,邵学广.团簇研究中的原子间势函数[J].化学进展,2005,17(4):588-594. Cai W S,Lin Y,Shao X G.Interatomic potential function in cluster research[J].Progress In Chemistry,2005,17(4):588-594.
[13] 张玲,张胜兰,艾君,等.基于势函数的均匀性度量与均匀性布点方法[J].湖北大学学报:自然科学版,2007,29(6):144-146. Zhang L,Zhang S L,Ai J,et al.Uniform measurement and uniform dots distribution based on potential function[J].Journal of Hubei University(Natural Science),2007,29(6):144-146.
[14] 孙彬,严卫东,张彤,等.良分布的多特征遥感图像自动配准算法[J].光电工程,2012,39(8):38-45. Sun B,Yan W D,Zhang T,et al.Remote sensing image automated registration algorithm based on multi-feature and well-distribution[J].Opto-Electronic Engineering,2012,39(8):38-45.
[15] 李伟峰,周金强,方胜辉.基于改进Hausdroff距离的图像配准方法[J].国土资源遥感,2014,26(2):93-98.doi:10.6046/gtzyyg.2014.02.16. Li W F,Zhou J Q,Fang S H.Image registration method based on improved Hausdorff distance[J].Remote Sensing for Land and Resources,2014,26(2):93-98.doi:10.6046/gtzyyg.2014.02.16.