Journal of Geodesy and Geoinformation Science ›› 2021, Vol. 4 ›› Issue (4): 97-112.doi: 10.11947/j.JGGS.2021.0408

Previous Articles     Next Articles

High-resolution Remote Sensing Image Semi-global Matching Method Considering Geometric Constraints of Connection Points and Image Texture Information

Jingguo LYU(),Xingbin YANG,Danlu ZHANG,Shan JIANG   

  1. Shool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
  • Received:2021-02-28 Accepted:2021-08-30 Online:2021-12-20 Published:2021-12-30
  • About author:Jingguo LYU, male, PhD, majors in geo-informatics and photogrammetry.E-mail: lvjingguo@bucea.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41871367);Ministry of Science and Technology of the People’s Republic of China(2018YFE0206100)

Abstract:

Dense matching of remote sensing images is a key step in the generation of accurate digital surface models. The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency, so it is widely used in close-range, aerial and satellite image matching. Based on the analysis of the original semi-global matching algorithm, this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture.
The method includes 4 parts: ① Precise orientation. Aiming at the system error in the image orientation model, the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images, and the sub-pixel positioning accuracy is obtained; ② Epipolar image generation. After the original image is divided into blocks, the epipolar image is generated based on the projection trajectory method; ③ Image dense matching. In order to reduce the size of the cost space and calculation time, the image is pyramided and then semi-globally matched layer by layer. In the matching process, the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects; ④ Generate DSM. In order to test the matching effect, the weighted median filter algorithm is used to filter the disparity map, and the DSM is obtained based on the principle of forward intersection. Finally, the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.

Key words: geometric constraints; image texture information; semi-global matching