论文标题
R2FD2：通过可重复的功能检测器和旋转不变的功能描述符快速且可靠的多模式遥感图像
R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor
论文作者
论文摘要
自动识别多模式图像之间的特征对应关系是面临巨大的挑战，因为辐射和几何形状都有显着差异。为了解决这些问题，我们提出了一种新颖的特征匹配方法（称为R2FD2），该方法对辐射和旋转差异很强。我们的R2FD2分为两个关键贡献，包括可重复的特征检测器和旋转不变的特征描述符。在第一阶段，提出了一个可重复的特征探测器，称为对数-Gabor（MALG）的多渠道自动相关，以进行特征检测，该功能检测将多渠道自动相关策略与对数gabor小波相结合，以检测兴趣点（IPS）与高重复性和均匀的分布和均匀的分布。在第二阶段，构建了一个旋转不变的特征描述符，称为旋转不变的最大索引图（RMLG），该图由两个组成部分组成：快速分配优势方向和特征表示的构建。在快速分配主要方向的过程中，构建了旋转不变的最大索引图（RMIM），以解决旋转变形。 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.此外，我们的R2FD2达到了两个像素内匹配的准确性，并且在匹配效率方面比其他最先进的方法具有很大的优势。
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.
