# 3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching arXiv: https://arxiv.org/abs/2404.00838 ยท HTML: https://mlsanity.com/paper/2404.00838/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 121 references that are in the map. - First posted on arXiv 1 Apr 2024. ## Builds on (most cited first, 30 of 38) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021) - [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018) - [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023) - [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020) - [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019) - [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019) - [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023) - [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017) - [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022) - [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017) - [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019) - [DKM: Dense Kernelized Feature Matching for Geometry Estimation](https://mlsanity.com/paper/2202.00667/) (2022) - [ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer](https://mlsanity.com/paper/2208.14201/) (2022) - [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015) - [SOSNet: Second Order Similarity Regularization for Local Descriptor Learning](https://mlsanity.com/paper/1904.05019/) (2019) - [Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed](https://mlsanity.com/paper/2403.04765/) (2024) - [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) - [ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction](https://mlsanity.com/paper/2112.02906/) (2021) - [DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching](https://mlsanity.com/paper/2308.08479/) (2023) - [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020) - [The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/1807.01569/) (2018) - [Online Invariance Selection for Local Feature Descriptors](https://mlsanity.com/paper/2007.08988/) (2020) - [Deep Lucas-Kanade Homography for Multimodal Image Alignment](https://mlsanity.com/paper/2104.11693/) (2021) - [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) - [ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning](https://mlsanity.com/paper/2205.07439/) (2022) ## Built on by (most cited first, 8 of 8) - [M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection](https://mlsanity.com/paper/2505.10931/) (2025) - [CRFT: Consistent-Recurrent Feature Flow Transformer for Cross-Modal Image Registration](https://mlsanity.com/paper/2604.05689/) (2026) - [RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2606.28266/) (2026) - [MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling](https://mlsanity.com/paper/2605.20090/) (2026) - [Label-Free Target-Domain Adaptation for Unconstrained Event-Image Feature Matching via Dual-Stage Distillation](https://mlsanity.com/paper/2607.10082/) (2026) - [LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching](https://mlsanity.com/paper/2608.04106/) (2026) - [Ex-Sim(3)-Reg: 2D-3D Correspondence Pruning via Extended Sim(3) Registration](https://mlsanity.com/paper/2608.28096/) (2026) - [GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation](https://mlsanity.com/paper/2609.37496/) (2026) ## Abstract (arXiv) Optical-SAR image matching is a fundamental task for image fusion and visual navigation. However, all large-scale open SAR dataset for methods development are collected from single platform, resulting in limited satellite types and spatial resolutions. Since images captured by different sensors vary significantly in both geometric and radiometric appearance, existing methods may fail to match corresponding regions containing the same content. Besides, most of existing datasets have not been categorized based on the characteristics of different scenes. To encourage the design of more general multi-modal image matching methods, we introduce a large-scale Multi-sources,Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching(3MOS). It consists of 155K optical-SAR image pairs, including SAR data from six commercial satellites, with resolutions ranging from 1.25m to 12.5m. The data has been classified into eight scenes including urban, rural, plains, hills, mountains, water, desert, and frozen earth. Extensively experiments show that none of state-of-the-art methods achieve consistently superior performance across different sources, resolutions and scenes. In addition, the distribution of data has a substantial impact on the matching capability of deep learning models, this proposes the domain adaptation challenge in optical-SAR image matching. Our data and code will be available at:https://github.com/3M-OS/3MOS.