Abstract: This paper proposes a quick and viewpoint-invariant matching method for oblique images. We preprocess an oblique image to obtain a rectified image that eliminates the geometric distortion, scale, and rotation of the image. First, we calculate the homography matrix between the oblique image and the object space plane by making full use of the Interior Orientation(IO) elements and the rough Exterior Orientation(EO) elements of the oblique image and recover the oblique image to a rectified image through 2D perspective transformation. Secondly, we extract the Harris corner-points from the rectified image and describe them using the SIFT descriptor. Thirdly, in order to distribute matches evenly and improve the matching efficiency during the matching process, we use the fundamental and homography matrices to calculate the potential area of the correct corresponding point of a Harris corner-point to be matched, and pick out all the extracted Harris corner-points in this potential area as candidate points. Nearest Neighbor Distance Ratio(NNDR) and Normalized Cross Correlation (NCC) measure constraints are used to get matches. Experiments conducted on three pairs of typical oblique images demonstrate that our method takes just a few seconds to match a pair of oblique images with a plenty of corresponding points distributed evenly with an extremely low mismatching rate.