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Co-registration of multimodal remote sensing images is still an ongoingchallenge because of nonlinear radiometric differences (NRD) and significantgeometric distortions (e.g., scale and rotation changes) between these images.In this paper, a robust matching method based on the Steerable filters isproposed consisting of two critical steps. First, to address severe NRD, anovel structural descriptor named the Steerable Filters of first- andsecond-Order Channels (SFOC) is constructed, which combines the first- andsecond-order gradient information by using the steerable filters with amulti-scale strategy to depict more discriminative structure features ofimages. Then, a fast similarity measure is established called Fast NormalizedCross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transformtechnique and the integral image to improve the matching efficiency.Furthermore, to achieve reliable registration performance, a coarse-to-finemultimodal registration system is designed consisting of two pivotal modules.The local coarse registration is first conducted by involving both detection ofinterest points (IPs) and local geometric correction, which effectivelyutilizes the prior georeferencing information of RS images to address globalgeometric distortions. In the fine registration stage, the proposed SFOC isused to resist significant NRD, and to detect control points between multimodalimages by a template matching scheme. The performance of the proposed matchingmethod has been evaluated with many different kinds of multimodal RS images.The results show its superior matching performance compared with thestate-of-the-art methods. Moreover, the designed registration system alsooutperforms the popular commercial software in both registration accuracy andcomputational efficiency. Our system is available athttps://github.com/yeyuanxin110.

A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients