ing at the problem of low matching accuracy and matching failure when using point features or line features alone in remote sensing images, a fully automatic registration algorithm for remote sensing images incorporating point and line complementary features is proposed. First, the improved scale-invariant feature transform (SIFT) algorithm is used to obtain the initial matching points, and the normalized cross-correlation (NCC) measure and the random sampling consistency algorithm are used to eliminate possible mismatches to obtain the points with the same name with high accuracy. Then, an improved line segment detection operator (LSD) is used to extract line segment features, determine candidate matching line segments and construct feature descriptors by known homography geometric transformation constraints and slope constraints, and then obtain line segments with the same name. Finally, the intersection point of the line segments with the same name is extracted, and the same-named point set of the first step is integrated to calculate the projection transformation parameters between the images to realize the image registration. Experimental results show that the proposed algorithm has significant advantages in matching accuracy and matching accuracy.