Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences. Our R2FD2 is conducted in two critical contributions, consisting of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which consists of two components: fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to depict a more discriminative feature representation, which improves RMLG's resistance to radiation and rotation variances.Experimental results show that the proposed R2FD2 outperforms five state-of-the-art feature matching methods, and has superior advantages in adaptability and universality. Moreover, our R2FD2 achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over other state-of-the-art methods.

翻译：自动识别多模态图像间的特征对应关系因辐射与几何差异显著而面临巨大挑战。为解决上述问题，我们提出一种对辐射与旋转差异具有鲁棒性的新型特征匹配方法（命名为R2FD2）。该方法由两大核心贡献构成：可重复特征检测器与旋转不变特征描述子。第一阶段提出基于对数Gabor多通道自相关（MALG）的可重复特征检测器，通过将多通道自相关策略与对数Gabor小波相结合，检测具有高重复性与均匀分布的兴趣点（IPs）。第二阶段构建旋转不变特征描述子——对数Gabor旋转不变最大索引图（RMLG），该描述子包含主方向快速分配与特征表征构建两个组件。在主方向快速分配过程中，构建旋转不变最大索引图（RMIM）以应对旋转变形。随后，所提出的RMLG将旋转不变的RMIM与DAISY空间配置相结合，刻画更具判别力的特征表征，从而增强RMLG对辐射与旋转变化的抗性。实验结果表明，所提出的R2FD2方法优于五种当前最优特征匹配方法，在适应性与普适性方面具有显著优势。此外，R2FD2实现了亚像素级匹配精度（误差小于两个像素），并在匹配效率上远超其他当前最优方法。