International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
THE RESEARCH OF LINE MATCHING ALGORITHM UNDER THE IMPROVED 
HOMOGRAPH MATRIX CONSTRAINT CONDITION 
Weixi Wang * *, Anying Lou, Jingxue Wang? 
* Shenzhen Urban Planning, Land and Resources Research Centre, Shanhai Building West, No.69, Xinwen Road, 
Futian District, Shenzhen, China, 518034 
? Geographic Information Engineering Institute of Jilin Province, No.716, Xinfa Road, Changchun, China, 130000 
* School of Geomatics, Liaoning Technical University, No.47, Xinhua Road, Fuxin, China, 123000 
Commission III/4 
KEY WORDS: Line Matching, Multi-Constraint Conditions, Homograph Matrix Constraint, Epipolar Constraint 
ABSTRACT: 
Focusing on the mismatching problems in line matching, this paper integrates the radiation information and the geometry 
information of the imagery as the multi-constraint conditions, and presents an improved line matching algorithm based on the 
improved homograph matrix constraint condition. This algorithm firstly obtains the homologous points by feature matching, and for 
each line to be matched, it calculates the homograph matrix with the homologous points in the neighbourhood of this line. And then 
it projects the line to be matched line in target image to the search image by the homograph matrix, and determines the candidate 
lines according to the distance between the central points of lines and the distance between two lines; In these candidate lines, this 
algorithm further determines the possible homologous lines according to the similarity constraints of the line angles, the distance 
from the origin of image to the lines, and the overlap of lines; Finally, the epipolar constraint is adopted to find out the overlap 
segments between homologous lines, and the real homologous line will be determined by the gray similarity constraint. This paper 
adopts the unmanned aerial vehicle images and UCX digital aerial images to carry on the experiments, and verifies the validity of 
the algorithm in this paper. 
1. INTRODUCTION 
Linear features are the important features of imagery, and also 
the important outlines of objects for their 3D reconstruction. 
Different with the point features, the linear features have richer 
image information, and are less affected by noise. Line 
Matching is simply finding the corresponding images of the 
same 3D line across two or multiple images of a scene. It is 
often the first step in the reconstruction of scenes such as an 
urban scene (Mosaddegh, 2008). 
Comparing with the point matching, the line matching has the 
following main advantages: (Dmore geometry constraints of 
linear features are used, and the matching results will be more 
reliable and in high accuracy; (2)linear features are not sensitive 
to noise in the extraction, and less affected by the geometric 
distortion and gray deformation of image. Linear features will 
effectively improve the accuracy of matching; (3)the number of 
line feature is far lower than the number of feature points, and 
the stereo matching based on linear features will greatly 
improve the efficiency of matching; (4)the linear features are 
easier for extraction and description. 
However, line matching is much more difficult to obtain a 
reliable matching result by single constraint. Mainly due to the 
following reasons: linear features commonly found in the edges 
of objects. In different view points, the linear features may 
appear occlusion, fracture, etc., and cause the image textures on 
both sides of lines would be different; for the same ground 
object, the direction of its lines projected into different images 
will also be different, so the candidate lines to be matched may 
  
* Corresponding author: Weixi Wang, Ph.D, E-mail: measurer@163.com. 
appear the results of “one-to-null” , “one-to-one” , “ one-to- 
multiple" and even “multiple-to-multiple” ; simultaneously, 
because of the incompleteness of linear feature extraction and 
the inconsistency of homologous line endpoints, the direction, 
length and texture features of lines cannot directly be used as 
the primitives in line matching. 
For now, the existing line matching algorithms can be divided 
into two categories: one is based on the structure information of 
linear features: mainly considering the geometry attributes 
ofthe line (length, degree of overlap, gradient, direction, 
location) in the matching process; The other is based on the 
dominant points of line: according to the dominant points, a line 
is divided into a number of discrete points, and the matching of 
feature line is achieved by matching these dominant points in it. 
Each algorithm has its own advantages and disadvantages 
at the same time. Due to the influence of various factors in the 
imaging process, as well as the complexity of line matching, it 
deserves to research a matching algorithm having high 
accuracy, excellent applicability, and good robustness. 
Fu Dan (2008) proposes a linear matching method based on the 
polar constraint and the RANSAC algorithm, and effectively 
solves the matching problem of partly occluded lines in the 
image. Li Tao (2008) proposes a robust and fast line matching 
algorithm based on the supporting region of line, which 
enhances the ability of this algorithm to adapt to the noise. 
However, these algorithms lack effective geometric constraints, 
and bear not only the complexity of their own, but also the low 
success rate of line matching. In this case, this paper integrates 
the radiation information and the geometry information of the 
  
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