Open AccessA least square matching optimization method of low altitude remote sensing images based on self-adaptive patch
Author(s) -
Nan Yang,
Yaping Zhang,
Jialin Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/585/1/012087
Subject(s) - epipolar geometry , computer vision , point cloud , matching (statistics) , computer science , pixel , point (geometry) , artificial intelligence , template matching , similarity (geometry) , mathematics , square (algebra) , algorithm , image (mathematics) , geometry , statistics
This paper presents a novel matching optimization method based on self-adaptive template. The proposed method is designed to be effective for enhancing the accuracy of stereo matching. In order to improve the similarity of the initial matching windows and fully exploit the pixels around the corresponding image points, a self-adaptive patch is introduced instead of a constant patch. Then, an error equation is built to compute the optimal point according to space geometry relationship and epipolar line constraint. At last, a least square adjustment method is used to calculate the coordinate of the corresponding 3D point in the Object Space Coordinate System. Comparison studies and experimental results prove the high accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.