# Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity

arXiv: https://arxiv.org/abs/2103.16871 · HTML: https://mlsanity.com/paper/2103.16871/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 54 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Built on by (most cited first, 26 of 26)

- [Deep Lucas-Kanade Homography for Multimodal Image Alignment](https://mlsanity.com/paper/2104.11693/) (2021)
- [RIFT2: Speeding-up RIFT with A New Rotation-Invariance Technique](https://mlsanity.com/paper/2303.00319/) (2023)
- [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)
- [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)
- [3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching](https://mlsanity.com/paper/2404.00838/) (2024)
- [Advances and Challenges in Multimodal Remote Sensing Image Registration](https://mlsanity.com/paper/2302.00912/) (2023)
- [PCNet: A Structure Similarity Enhancement Method for Multispectral and Multimodal Image Registration](https://mlsanity.com/paper/2106.05124/) (2021)
- [MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching](https://mlsanity.com/paper/2501.11299/) (2025)
- [PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment](https://mlsanity.com/paper/2303.11526/) (2023)
- [MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration](https://mlsanity.com/paper/2204.00260/) (2022)
- [L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery](https://mlsanity.com/paper/2304.06871/) (2023)
- [Deep Loss Convexification for Learning Iterative Models](https://mlsanity.com/paper/2411.10649/) (2024)
- [Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images](https://mlsanity.com/paper/2302.14239/) (2023)
- [Invisible Backdoor Attack Through Singular Value Decomposition](https://mlsanity.com/paper/2403.13018/) (2024)
- [A Robust Multisource Remote Sensing Image Matching Method Utilizing Attention and Feature Enhancement Against Noise Interference](https://mlsanity.com/paper/2410.11848/) (2024)
- [PromptMID: Modal Invariant Descriptors Based on Diffusion and Vision Foundation Models for Optical-SAR Image Matching](https://mlsanity.com/paper/2502.18104/) (2025)
- [Multimodal Image Matching based on Frequency-domain Information of Local Energy Response](https://mlsanity.com/paper/2503.20827/) (2025)
- [SA-DNet: A on-demand semantic object registration network adapting to non-rigid deformation](https://mlsanity.com/paper/2210.09900/) (2022)
- [SATBA: An Invisible Backdoor Attack Based On Spatial Attention](https://mlsanity.com/paper/2302.13056/) (2023)
- [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)
- [A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method](https://mlsanity.com/paper/2508.10294/) (2025)
- [Matching of SAR and optical images based on transformation to shared modality](https://mlsanity.com/paper/2602.12515/) (2026)
- [TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images](https://mlsanity.com/paper/2605.12064/) (2026)
- [GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval](https://mlsanity.com/paper/2605.19734/) (2026)
- [Radiation, Rotation and Scale Invariant Feature Descriptor for Multimodal Image Matching](https://mlsanity.com/paper/2609.06343/) (2026)

## Abstract (arXiv)

Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation, and use the extended model to build HOPCncc. Then a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images, and has been tested with a variety of optical, LiDAR, SAR and map data. The results show that HOPCncc is robust against complex non-linear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration.