# Semi-supervised Multiscale Matching for SAR-Optical Image

arXiv: https://arxiv.org/abs/2508.07812 · HTML: https://mlsanity.com/paper/2508.07812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [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)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Improving Co-registration for Sentinel-1 SAR and Sentinel-2 Optical images](https://mlsanity.com/paper/2005.11092/) (2020)
- [Deep Learning Improves Template Matching by Normalized Cross Correlation](https://mlsanity.com/paper/1705.08593/) (2017)
- [Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network](https://mlsanity.com/paper/1810.06470/) (2018)
- [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)

## Abstract (arXiv)

Driven by the complementary nature of optical and synthetic aperture radar (SAR) images, SAR-optical image matching has garnered significant interest. Most existing SAR-optical image matching methods aim to capture effective matching features by employing the supervision of pixel-level matched correspondences within SAR-optical image pairs, which, however, suffers from time-consuming and complex manual annotation, making it difficult to collect sufficient labeled SAR-optical image pairs. To handle this, we design a semi-supervised SAR-optical image matching pipeline that leverages both scarce labeled and abundant unlabeled image pairs and propose a semi-supervised multiscale matching for SAR-optical image matching (S2M2-SAR). Specifically, we pseudo-label those unlabeled SAR-optical image pairs with pseudo ground-truth similarity heatmaps by combining both deep and shallow level matching results, and train the matching model by employing labeled and pseudo-labeled similarity heatmaps. In addition, we introduce a cross-modal feature enhancement module trained using a cross-modality mutual independence loss, which requires no ground-truth labels. This unsupervised objective promotes the separation of modality-shared and modality-specific features by encouraging statistical independence between them, enabling effective feature disentanglement across optical and SAR modalities. To evaluate the effectiveness of S2M2-SAR, we compare it with existing competitors on benchmark datasets. Experimental results demonstrate that S2M2-SAR not only surpasses existing semi-supervised methods but also achieves performance competitive with fully supervised SOTA methods, demonstrating its efficiency and practical potential.