A line feature matching method for high-resolution images was proposed to improve the low significant level of a point feature and to overcome the matching shortage between weak texture images. Firstly, the edges of images were extracted and tracked to fit straight-lines. All straight-lines were classified into two groups: long-lines and short-lines. The long-lines were matched based on the direction relationship primarily. Then, the relationship-descriptors of short-lines were constructed using the angle and the Euclidean distance between the short-line and long correspondences. Finally, short-lines were matched according to the similarity of their relationship-descriptors. The experimental results demonstrate that the proposed line matching algorithm is robust for the scale, rotation and illumination. As all the lines have corresponding linear equations in the same image, the image feature has higher significant level and can avoid the mismatching. The probability of correct matches of the algorithm exceeds 90% and its root mean square error has achieved sub-pixel level. The performance of the proposed algorithm is better than that of the point-based method, especially in weak texture areas.