Accurate Affine Invariant Image Matching Using Oriented Least Square

Image matching is a vital process for many photogrammetric and remote sensing applications such as image registration and aerial triangulation. In this paper, an accurate affine invariant image matching approach is presented. The proposed approach consists of three main steps. In the first step, two affine invariant feature detectors, including MSER and Harris-Affine features are applied for feature extraction. In the second step, initial corresponding features are selected using Euclidean distance between feature descriptors, followed by a consistency check process. Finally to overcome low positional accuracy of the local affine feature, an advanced version of the least square matching (ism) namely,, Oriented Least Square Matching (oLsm) is developed. Well-known LSM method has been widely accepted as one of the most accurate methods to obtain high reliable corresponding points from a stereo image pair. However, it is sensitive to significant geometric distortion and requires very good initial approximation. In the proposed OLSM method, shape and size of the matching window are appropriately approximated using obtained affine shape information of the initial elliptical feature pairs. The proposed method was successfully applied for matching various synthetic and real close range and satellite images. Results demonstrate its accuracy and capability compared to standard LSM method.