Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 179-184, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-179-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 179-184, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-179-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

AN IMPROVED IMAGE MATCHING METHOD BASED ON SURF ALGORITHM

S. J. Chen1, S. Z. Zheng2, Z. G. Xu1, C. C. Guo2, and X. L. Ma3 S. J. Chen et al.
  • 1School of Resource Engineering, Longyan University, Longyan, China
  • 2College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
  • 3Chinese Academy of Surveying and Mapping, Beijing, China

Keywords: Image matching, Information entropy, SURF, Delaunay Triangulation

Abstract. Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.