Fast and Robust Optical-to-SAR Remote Sensing Image Registration Using Region-Aware Phase Descriptor
Yibin Ye, Qinwei Wang, Hong Zhao, Xichao Teng, Yijie Bian, Zhang Li
Abstract
Automatic registration of optical and synthetic aperture radar (SAR) remote sensing images has been extensively researched for decades. However, fast and robust multimodal image registration is still a challenge due to the significant region differences in many remote sensing scenarios (e.g. city and sea region coexist in single harbor imaging). To address this problem, this paper proposes a novel optical-to-SAR registration method called FED-HOPC, which integrates the fast Fourier transform (FFT) and weighted edge density (WED) map into the histogram of oriented phase congruency (HOPC) based registration framework. FFT is used to accelerate the 3D normalized cross-correlation (NCC) for the coarse matching. WED map is employed to detect adaptive block-based interest points, which are used to build region aware HOPC descriptors for the following fine registration. To evaluate the performance of FED-HOPC, extensive experiments on four optical-to-SAR registration datasets are conducted. The results demonstrate that FED-HOPC effectively register optical-to-SAR images with significant region differences and outperforms several state-of-the-art methods, including HOPC, MIND, MIRD, and CFOG.