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

arXiv: https://arxiv.org/abs/2202.13347 · HTML: https://mlsanity.com/paper/2202.13347/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 64 references that are in the map.
- First posted on arXiv 27 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Understanding Convolution for Semantic Segmentation](https://mlsanity.com/paper/1702.08502/) (2017)
- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)
- [Fast and Robust Matching for Multimodal Remote Sensing Image Registration](https://mlsanity.com/paper/1808.06194/) (2018)
- [Improving Co-registration for Sentinel-1 SAR and Sentinel-2 Optical images](https://mlsanity.com/paper/2005.11092/) (2020)
- [Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales](https://mlsanity.com/paper/1602.02720/) (2016)

## Built on by (most cited first, 10 of 10)

- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2302.05109/) (2023)
- [R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor](https://mlsanity.com/paper/2212.02277/) (2022)
- [Advances and Challenges in Multimodal Remote Sensing Image Registration](https://mlsanity.com/paper/2302.00912/) (2023)
- [GDROS: A Geometry-Guided Dense Registration Framework for Optical-SAR Images under Large Geometric Transformations](https://mlsanity.com/paper/2511.00598/) (2025)
- [PromptMID: Modal Invariant Descriptors Based on Diffusion and Vision Foundation Models for Optical-SAR Image Matching](https://mlsanity.com/paper/2502.18104/) (2025)
- [REMM:Rotation-Equivariant Framework for End-to-End Multimodal Image Matching](https://mlsanity.com/paper/2407.11637/) (2024)
- [Cross Spatial Temporal Fusion Attention for Remote Sensing Object Detection via Image Feature Matching](https://mlsanity.com/paper/2507.19118/) (2025)
- [Hybrid second-order gradient histogram based global low-rank sparse regression for robust face recognition](https://mlsanity.com/paper/2511.05893/) (2025)
- [Multi-Sensor Matching with HyperNetworks](https://mlsanity.com/paper/2601.12325/) (2026)

## Abstract (arXiv)

Co-registration of multimodal remote sensing images is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110.