  Automatic registration of multimodal remote sensing data (e.g., optical,
LiDAR, SAR) is a challenging task due to the significant non-linear radiometric
differences between these data. To address this problem, this paper proposes a
novel feature descriptor named the Histogram of Orientated Phase Congruency
(HOPC), which is based on the structural properties of images. Furthermore, a
similarity metric named HOPCncc is defined, which uses the normalized
correlation coefficient (NCC) of the HOPC descriptors for multimodal
registration. In the definition of the proposed similarity metric, we first
extend the phase congruency model to generate its orientation representation,
and use the extended model to build HOPCncc. Then a fast template matching
scheme for this metric is designed to detect the control points between images.
The proposed HOPCncc aims to capture the structural similarity between images,
and has been tested with a variety of optical, LiDAR, SAR and map data. The
results show that HOPCncc is robust against complex non-linear radiometric
differences and outperforms the state-of-the-art similarities metrics (i.e.,
NCC and mutual information) in matching performance. Moreover, a robust
registration method is also proposed in this paper based on HOPCncc, which is
evaluated using six pairs of multimodal remote sensing images. The experimental
results demonstrate the effectiveness of the proposed method for multimodal
image registration.
