A Novel Multiscale Adaptive Binning Phase Congruency Feature for SAR and Optical Image Registration
Jianwei Fan, Yuanxin Ye, Jian Li, Guichi Liu, Yanling Li
Abstract
Automatic registration of synthetic aperture radar (SAR) and optical images is still a challenging problem because of the potential differences in geometric and intensity. In this work, we propose a robust and efficient method for improving the registration performance of SAR and optical images. Our work consists mainly of two steps, including the feature point detection stage and the feature description stage. In the first stage, we present a new feature extraction method, named nonlinear diffusion-based Harris-Laplace (NDHL) detector, which incorporates the nonlinear diffusion and a spatial consistency strategy into the Harris-Laplace detector, which can weaken the modality variations and enhance the structural features of the multimodal images, and can detect many more similar and highly repeatable feature points. In the second stage, we design a novel structural descriptor, named multiscale adaptive binning phase congruency (MABPC). The proposed MABPC descriptor encodes multiscale phase congruency features with an adaptive binning spatial structure, which brings an improvement of the robustness against geometric and nonlinear intensity discrepancies. Experimental results on both simulated and real image pairs show that the proposed registration method achieves encouraging performance improvements over other state-of-the-art methods. In comparison with the ROS-PC and RIFT methods, the proposed method obtains an average 54.6% improvement for the number of correct matches and has an average registration precision of 1.38 pixels.