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XIANG Yuming, CHEN Jinyang, HONG Zhonghua, et al. OSDataset2.0: sar-optical image matching dataset and evaluation benchmark[J]. Journal of Radars, in press. doi: 10.12000/JR25176
Citation: XIANG Yuming, CHEN Jinyang, HONG Zhonghua, et al. OSDataset2.0: sar-optical image matching dataset and evaluation benchmark[J]. Journal of Radars, in press. doi: 10.12000/JR25176
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OSDataset2.0: SAR-Optical Image Matching Dataset and Evaluation Benchmark

DOI: 10.12000/JR25176 CSTR: 32380.14.J25176
  • 1.

    College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China

  • 2.

    Shanghai Key Laboratory of Space Navigation and Positioning Techniques for Aerospace Mapping and Remote Sensing, Shanghai 200092, China

  • 3.

    College of Information Science and Technology, Shanghai Ocean University, Shanghai 201306, China

  • 4.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100194, China

Funds:  Shanghai Science and Technology Program Project (2024CSJZN01300), National Key Laboratory of Microwave Imaging Foundation
More Information
  • Corresponding author: HONG Zhonghua
  • Received Date: 2025-09-16
    Available Online: 2025-12-13
    Abstract
  • Synthetic aperture radar (SAR) and optical imagery are two key remote-sensing modalities in Earth observation, and cross-modal image matching between them is widely applied in tasks such as image fusion, joint interpretation, and high-precision geolocation. In recent years, with the rapid growth of Earth-observation data, the importance of cross-modal image matching between SAR and optical data has become increasingly prominent, and related studies have achieved notable progress. In particular, deep learning (DL)-based methods, owing to their strengths in cross-modal feature representation and high-level semantic extraction, have demonstrated excellent matching accuracy and adaptability across varying imaging conditions. However, most publicly available datasets are limited to small image patches and lack complete full-scene image pairs that cover realistic large-scale scenarios, making it difficult to comprehensively evaluate the performance of matching algorithms in practical remote-sensing settings and constraining advances in the training and generalization of DL models. To address these issues, this study develops and releases OSDataset2.0, a large-scale benchmark dataset for SAR-optical image matching. The dataset comprises two parts: a patch-level subset and a scene-level subset. The patch-level subset is composed of 6,476 registered 512 × 512 image pairs covering 14 countries (Argentina, Australia, Poland, Germany, Russia, France, Qatar, Malaysia, the United States, Japan, Türkiye, Singapore, India, and China); the scene-level subset consists of one pair of full-scene optical and SAR images. For full-scene images, high-precision, uniformly distributed ground-truth correspondences are provided, extracted under the principle of imaging-mechanism consistency, together with a general evaluation codebase that supports quantitative analysis of registration accuracy for arbitrary matching algorithms. To further assess the dataset’s effectiveness and challenge level, a systematic evaluation of 11 representative optical–SAR matching methods on OSDataset2.0 is conducted, covering traditional feature-based approaches and mainstream DL models. Experimental results show that the dataset not only supports effective algorithmic comparisons but also provides reliable training resources and a unified evaluation benchmark for subsequent research.

     

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