# Advances and Challenges in Multimodal Remote Sensing Image Registration

arXiv: https://arxiv.org/abs/2302.00912 · HTML: https://mlsanity.com/paper/2302.00912/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 67 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes](https://mlsanity.com/paper/2110.11590/) (2021)
- [The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/1807.01569/) (2018)
- [The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/2103.08259/) (2021)
- [Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity](https://mlsanity.com/paper/2103.16871/) (2021)
- [Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN](https://mlsanity.com/paper/1801.08467/) (2018)
- [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)
- [The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area](https://mlsanity.com/paper/1801.07532/) (2018)
- [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)
- [Fast and Robust Registration of Aerial Images and LiDAR data Based on Structrual Features and 3D Phase Correlation](https://mlsanity.com/paper/2004.09811/) (2020)

## Built on by (most cited first, 7 of 7)

- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching](https://mlsanity.com/paper/2404.00838/) (2024)
- [Grid-Reg: Detector-Free Gridized Feature Learning and Matching for Large-Scale SAR-Optical Image Registration](https://mlsanity.com/paper/2507.04233/) (2025)
- [Compress-Align-Detect: onboard change detection from unregistered images](https://mlsanity.com/paper/2507.15578/) (2025)
- [Unregistered Spectral Image Fusion: Unmixing, Adversarial Learning, and Recoverability](https://mlsanity.com/paper/2603.21510/) (2026)
- [REMM:Rotation-Equivariant Framework for End-to-End Multimodal Image Matching](https://mlsanity.com/paper/2407.11637/) (2024)
- [Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking](https://mlsanity.com/paper/2512.00052/) (2025)

## Abstract (arXiv)

Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e.g., optical sensors) to the new generation of multimodal sensors [e.g., multispectral, hyperspectral, light detection and ranging (LiDAR) and synthetic aperture radar (SAR) sensors]. These advanced devices can dynamically provide various and abundant multimodal remote sensing images with different spatial, temporal, and spectral resolutions according to different application requirements. Since then, it is of great scientific significance to carry out the research of multimodal remote sensing image registration, which is a crucial step for integrating the complementary information among multimodal data and making comprehensive observations and analysis of the Earths surface. In this work, we will present our own contributions to the field of multimodal image registration, summarize the advantages and limitations of existing multimodal image registration methods, and then discuss the remaining challenges and make a forward-looking prospect for the future development of the field.