Robust Point Correspondence with Gabor Scale-Invariant Feature Transform for Optical Satellite Image Registration

Abstract

In this paper, a robust point correspondence algorithm is proposed to address the problems with SIFT-like methods for the optical satellite image registration. SIFT-like methods involve two issues that are rarely noticed: non-orientation selectivity in feature detection and feature redundancy in feature description. The novelty of the proposed approach is that the advantages of biologically motivated methods are adopted to resolve above two problems. Firstly, by using a 2D Gabor filter bank to model the visual cognitive computational model, intuitive and robust keypoints are detected. The proposed detector can capture salient visual properties such as the orientation and spatial frequency selectivity. Secondly, multi-characteristic scales of the keypoints are selected based on the Gabor kernel function, and then multi-feature descriptors with high discriminating power are defined. By using the proposed detector and descriptor, the feature redundancy can translate into benefits. Finally, a feature matching strategy for multi-feature descriptors is designed, to improve the reliability of feature matching. Evaluation criteria of 1-precision, RMSE and visual inspection of the matched pairs are used to demonstrate the superior performance of the proposed algorithm on optical satellite image registration.