SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
94
17 commits
updated Nov 21, 2025
README
【Latest Recommended Paper】
T. Zhang, X. Zhang, and G. Gao, "Divergence to Concentration and Population to Individual: A Progressive Approaching Ship Detection Paradigm for Synthetic Aperture Radar Remote Sensing Imagery," IEEE Trans. Aerosp. Electron. Syst., pp. 1-13, 2025.
https://doi.org/10.1109/TAES.2025.3631066
What's New? 🚨
📢 Call for Papers: Two Hot Special Issues in Remote Sensing & Marine Science
- Remote Sensing (MDPI)
- Journal: Remote Sensing 📡 (IF≈4.8, JCR Q1)
- Special Issue: Advances in SAR, Optical, Hyperspectral and Infrared Remote Sensing 🌍
- Learn more & Submit: Special Issue Page 🔗
- Frontiers in Marine Science
- Journal: Frontiers in Marine Science 📚 (IF=3.0, JCR Q1)
- Section: Ocean Observation 🌊
- Research Topic: Ocean Object Surveillance Using Satellite Synthetic Aperture Radar 🛰
- Learn more & Submit: Research Topic Page 🔗
【SSDD】 SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
https://drive.google.com/file/d/1glNJUGotrbEyk43twwB9556AdngJsynZ/view?usp=sharing
https://pan.baidu.com/s/1Lpg28ZvMSgNXq00abHMZ5Q password: 2021
Please cite this paper:
T. Zhang et al., "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis," Remote Sens., vol. 13, no. 18, pp. 1–41, 2021, Art. no. 3690.
【SL-SSDD】
SL-SSDD: Sea-Land Segmentation Dataset for SSDD SL-SSDD is the first synergistic sea-land segmentation dataset tailored for deep learning-based SAR ship detection, built upon the well-established SAR Ship Detection Dataset (SSDD). It addresses the critical gap of lacking sea-land prior information in existing SAR ship detection datasets, enabling models to fully distinguish between sea and land regions for more accurate detection.
Download & Citation Dataset Link: https://github.com/Han-Ke/SL-SSDD
Please cite this paper: Ke, H.; Ke, X.; Zhang, Z.; Chen, X.; Xu, X.; Zhang, T. SLA-Net: A Novel Sea–Land Aware Network for Accurate SAR Ship Detection Guided by Hierarchical Attention Mechanism. Remote Sens. 2025, 17, 3576. https://doi.org/10.3390/rs17213576
Similar repos
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
94
17 commits
updated Nov 21, 2025
README
【Latest Recommended Paper】
T. Zhang, X. Zhang, and G. Gao, "Divergence to Concentration and Population to Individual: A Progressive Approaching Ship Detection Paradigm for Synthetic Aperture Radar Remote Sensing Imagery," IEEE Trans. Aerosp. Electron. Syst., pp. 1-13, 2025.
https://doi.org/10.1109/TAES.2025.3631066
What's New? 🚨
📢 Call for Papers: Two Hot Special Issues in Remote Sensing & Marine Science
- Remote Sensing (MDPI)
- Journal: Remote Sensing 📡 (IF≈4.8, JCR Q1)
- Special Issue: Advances in SAR, Optical, Hyperspectral and Infrared Remote Sensing 🌍
- Learn more & Submit: Special Issue Page 🔗
- Frontiers in Marine Science
- Journal: Frontiers in Marine Science 📚 (IF=3.0, JCR Q1)
- Section: Ocean Observation 🌊
- Research Topic: Ocean Object Surveillance Using Satellite Synthetic Aperture Radar 🛰
- Learn more & Submit: Research Topic Page 🔗
【SSDD】 SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
https://drive.google.com/file/d/1glNJUGotrbEyk43twwB9556AdngJsynZ/view?usp=sharing
https://pan.baidu.com/s/1Lpg28ZvMSgNXq00abHMZ5Q password: 2021
Please cite this paper:
T. Zhang et al., "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis," Remote Sens., vol. 13, no. 18, pp. 1–41, 2021, Art. no. 3690.
【SL-SSDD】
SL-SSDD: Sea-Land Segmentation Dataset for SSDD SL-SSDD is the first synergistic sea-land segmentation dataset tailored for deep learning-based SAR ship detection, built upon the well-established SAR Ship Detection Dataset (SSDD). It addresses the critical gap of lacking sea-land prior information in existing SAR ship detection datasets, enabling models to fully distinguish between sea and land regions for more accurate detection.
Download & Citation Dataset Link: https://github.com/Han-Ke/SL-SSDD
Please cite this paper: Ke, H.; Ke, X.; Zhang, Z.; Chen, X.; Xu, X.; Zhang, T. SLA-Net: A Novel Sea–Land Aware Network for Accurate SAR Ship Detection Guided by Hierarchical Attention Mechanism. Remote Sens. 2025, 17, 3576. https://doi.org/10.3390/rs17213576