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

arXiv: https://arxiv.org/abs/2212.02277 · HTML: https://mlsanity.com/paper/2212.02277/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)
- [ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning](https://mlsanity.com/paper/2205.07439/) (2022)
- [A Robust Multimodal Remote Sensing Image Registration Method and System Using Steerable Filters with First- and Second-order Gradients](https://mlsanity.com/paper/2202.13347/) (2022)
- [MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration](https://mlsanity.com/paper/2204.00260/) (2022)

## Built on by (most cited first, 8 of 8)

- [Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2302.05109/) (2023)
- [View Transformation Robustness for Multi-View 3D Object Reconstruction with Reconstruction Error-Guided View Selection](https://mlsanity.com/paper/2412.11428/) (2024)
- [UFM: Unified Feature Matching Pre-training with Multi-Modal Image Assistants](https://mlsanity.com/paper/2503.21820/) (2025)
- [LostNet: A smart way for lost and find](https://mlsanity.com/paper/2301.02277/) (2023)
- [Exploiting Neighborhood Structural Features for Change Detection](https://mlsanity.com/paper/2302.05114/) (2023)
- [Multi-Sensor Matching with HyperNetworks](https://mlsanity.com/paper/2601.12325/) (2026)
- [DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection](https://mlsanity.com/paper/2605.07151/) (2026)
- [Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching](https://mlsanity.com/paper/2609.06343/) (2026)

## Abstract (arXiv)

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.