R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor

• URL: http://arxiv.org/abs/2212.02277v2
• Date: Tue, 6 Dec 2022 07:05:04 GMT
• Title: R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor
• Authors: Bai Zhu, Chao Yang, Jinkun Dai, Jianwei Fan, Yuanxin Ye
• Abstract summary: We propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences. The proposed R2FD2 outperforms five state-of-the-art feature matching methods, and has superior advantages in universality and adaptability. 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.
• Score: 3.395266574804949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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