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SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis

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**【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 
1. **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](https://www.mdpi.com/journal/remotesensing/special_issues/Q8C5625K5L) 🔗
2. **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](https://www.frontiersin.org/research-topics/76046/ocean-object-surveillance-using-satellite-synthetic-aperture-radar) 🔗

**【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

Official-SSDD

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