The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 103–109, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-103-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 103–109, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-103-2020

  12 Aug 2020

12 Aug 2020

LINE SEGMENT MATCHING ALGORITHM BASED ON FEATURE GROUPING AND LBD DESCRIPTOR

J. X. Wang1, W. X. Wang2, C. Y. Wang1, H. Zhu3, W. Y. He1, and S. Y. Liu1 J. X. Wang et al.
  • 1School of Geomatics, Liaoning Technical Universtiy, Fuxin, China
  • 2Research Institute for Smart Cities, Shenzhen University, Shenzhen, China
  • 3College of Ecology and Environment, Institute of Disaster Prevention, Beijing, China

Keywords: Image matching, Line segment matching, Pairwise line matching, Line band descriptor, Epipolar constraint

Abstract. This paper proposes a line-matching algorithm based on feature grouping and a line band descriptor (LBD) to address the insufficient reliability of individual line descriptors for line matching. First, the algorithm generates line-pairs according to geometrical relationships such as the distances and angles between line segments extracted from a single image. Subsequently, the algorithm employs the epipolar line of intersection between two lines in a reference line-pair to constrain candidate pairs corresponding to the reference line-pair. Thereafter, each line in the reference line-pair is considered individually, and its support region and the corresponding support region of each candidate line in the candidate pairs are established, following which an affine transformation is used for unifying the sizes of the reference support region and the candidate support region. Moreover, the LBD descriptor is then used for describing the reference and candidate lines. The Euclidean distances between the reference line and each candidate line descriptors are calculated, and the nearest neighbor distance ratio (NNDR) is used as a criterion for determining the final matching. Finally, the one-to-many and many-to-one line correspondences in matching results are transformed into one-to-one line correspondences by fitting multiple lines to the new line; simultaneously, incorrect matches are eliminated. The experimental results show that the proposed algorithm yields reliable line-matching performance for close-range images.