A hierarchical approach to image registration using feature consensus and hausdorff distance

Abstract

This paper presents a new hybrid and hierarchical algorithm for aligning two partially overlapping aerial images. This computationally efficient approach produces accurate results even when large rotation and translation have occurred between two images. The first step of the approach is coarse matching where transformation parameters are estimated using the partial Hausdorff distance measure for maximally feature consensus. For feature extraction, it applies a modified phase congruency model to effectively locate feature points of local curvature discontinuity, structural boundaries, and other prominent edges. Our proposed coarse matching doesn't require explicit feature correspondence, and the partial Hausdorff distance measure can tolerate well the presence of outliers and feature extraction errors. In the second step, the pairwise matching of the feature points detected from both images is performed, where the initial estimate obtained in the first step is used to dramatically facilitate the determination of feature point correspondence. This two-step approach compensates deficiencies in each step and it is computationally efficient. The first step dramatically decreases the size of the search range for correspondence establishment in the second step and no direct pairwise feature matching is required in the first step. Experiment results demonstrate the robustness of our proposed algorithm using real aerial photos.