  Co-registration of multimodal remote sensing images is still an ongoing
challenge because of nonlinear radiometric differences (NRD) and significant
geometric distortions (e.g., scale and rotation changes) between these images.
In this paper, a robust matching method based on the Steerable filters is
proposed consisting of two critical steps. First, to address severe NRD, a
novel structural descriptor named the Steerable Filters of first- and
second-Order Channels (SFOC) is constructed, which combines the first- and
second-order gradient information by using the steerable filters with a
multi-scale strategy to depict more discriminative structure features of
images. Then, a fast similarity measure is established called Fast Normalized
Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform
technique and the integral image to improve the matching efficiency.
Furthermore, to achieve reliable registration performance, a coarse-to-fine
multimodal registration system is designed consisting of two pivotal modules.
The local coarse registration is first conducted by involving both detection of
interest points (IPs) and local geometric correction, which effectively
utilizes the prior georeferencing information of RS images to address global
geometric distortions. In the fine registration stage, the proposed SFOC is
used to resist significant NRD, and to detect control points between multimodal
images by a template matching scheme. The performance of the proposed matching
method has been evaluated with many different kinds of multimodal RS images.
The results show its superior matching performance compared with the
state-of-the-art methods. Moreover, the designed registration system also
outperforms the popular commercial software in both registration accuracy and
computational efficiency. Our system is available at
https://github.com/yeyuanxin110.
