L-2-SIFT: SIFT feature extraction and matching for large images in   large-scale aerial photogrammetry

doi:10.1016/j.isprsjprs.2014.02.001

CORC > 北京大学 > 地球与空间科学学院

	L-2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry
	Sun, Yanbiao ; Zhao, Liang ; Huang, Shoudong ; Yan, Lei ; Dissanayake, Gamini
刊名	isprs journal of photogrammetry and remote sensing
	2014
关键词	Aerial photogrammetry Large-scale SIFT Feature extraction and matching Bundle adjustment DESCRIPTORS
DOI	10.1016/j.isprsjprs.2014.02.001
英文摘要	The primary contribution of this paper is an efficient feature extraction and matching implementation for large images in large-scale aerial photogrammetry experiments. First, a Block-SIFT method is designed to overcome the memory limitation of SIFT for extracting and matching features from large photogrammetric images. For each pair of images, the original large image is split into blocks and the possible corresponding blocks in the other image are determined by pre-estimating the relative transformation between the two images. Because of the reduced memory requirement, features can be extracted and matched from the original images without down-sampling. Next, a red-black tree data structure is applied to create a feature relationship to reduce the search complexity when matching tie points. Meanwhile, tree key exchange and segment matching methods are proposed to match the tie points along-track and across-track. Finally, to evaluate the accuracy of the features extracted and matched from the proposed L-2-SIFT algorithm, a bundle adjustment with parallax angle feature parametrization (ParallaxBA(1)) is applied to obtain the Mean Square Error (MSE) of the feature reprojections, where the feature extraction and matching result is the only information used in the nonlinear optimisation system. Seven different experimental aerial photogrammetric datasets are used to demonstrate the efficiency and validity of the proposed algorithm. It is demonstrated that more than 33 million features can be extracted and matched from the Taian dataset with 737 images within 21 h using the L-2-SIFT algorithm. In addition, the ParallaxBA involving more than 2.7 million features and 6 million image points can easily converge to an MSE of 0.03874. The C/C++ source code for the proposed algorithm is available at http://services.eng.uts.edu.au/sdhuang/research.htm. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000335104700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology; SCI(E); EI; 9; ARTICLE; syb51@pku.edu.cn; Liang.Zhao-1@uts.edu.au; Shoudong.Huang@uts.edu.au; lyan@pku.edu.cn; Dissanayake@uts.edu.au; 1-16; 91
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/155481]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Sun, Yanbiao,Zhao, Liang,Huang, Shoudong,et al. L-2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry[J]. isprs journal of photogrammetry and remote sensing,2014.
APA	Sun, Yanbiao,Zhao, Liang,Huang, Shoudong,Yan, Lei,&Dissanayake, Gamini.(2014).L-2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry.isprs journal of photogrammetry and remote sensing.
MLA	Sun, Yanbiao,et al."L-2-SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry".isprs journal of photogrammetry and remote sensing (2014).